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314690db30e0da820145d5cd1eef3bc5 | 104 141 | 2.1 Normative references | Normative references are not applicable in the present document. |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 2.2 Informative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks included in this clause were valid at the time of publication ETSI cannot guarantee their long-term validity. The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's understanding, but are not required for conformance to the present document. [i.1] ETSI GR mWT 028 (V1.1.1): "New KPI's for planning microwave and millimetre wave backhaul network". [i.2] NGMN 0.4.2 FINAL (July 2011): "Guidelines for LTE Backhaul Traffic Estimation". [i.3] A. K. Gupta and S. Nadarajah: "Handbook of Beta Distribution and Its Applications", Boca Raton, FL, USA: CRC Press, 2004. [i.4] Recommendation ITU-R P.530-19 (09/2025): "Propagation data and prediction methods required for the design of terrestrial line-of-sight systems". [i.5] Recommendation ITU-R P.676-13 (08/2022): "Attenuation by atmospheric gases and related effects". ETSI ETSI TR 104 141 V1.1.1 (2026-03) 10 |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 3 Definition of terms, symbols and abbreviations | |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 3.1 Terms | For the purposes of the present document, the following terms apply: peak (aggregated) traffic demand: maximum value of the aggregated traffic demand process experienced by a given backhaul link |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 3.2 Symbols | For the purposes of the present document, the following symbols apply: , , , ℓ generic indices ∈ is an element of set Backhaul Traffic Availability total number of capacities that can be delivered by a given backhaul link th capacity that can be delivered by a given backhaul link (with () < () < . . . < ()) link failure state () availability of the th backhaul capacity ( ) probability that event occurs random variable representing the aggregated traffic demand of a given backhaul link cumulative distribution function of a generic random variable , evaluated at the argument , , probability density function of a generic Beta-distributed random variable with parameters and , evaluated at the argument first shape parameter of the Beta distribution second shape parameter of the Beta distribution Γ gamma function evaluated at the argument , , cumulative distribution function of a generic Beta-distributed random variable with parameters and , evaluated at the argument , , regularized incomplete Beta function with parameters and , evaluated at the argument expected value of a generic random variable exponential function evaluated at the argument () integral of a generic real-valued function () with respect to the real variable on an interval [ , ] ! maximum (or peak) value of the generic aggregated traffic demand ℳ continuous subspace of the re-scaled Beta distributions family where parameters and are related according to equation (10) normalized average traffic demand value ETSI ETSI TR 104 141 V1.1.1 (2026-03) 11 " total number of possible values for the shape parameter in the analytical procedure for deriving BTA lower bounds #th possible value for the shape parameter $ discrete set of " possible values for the shape parameter #th possible value for the shape parameter % discrete set that contains all the pairs (, ) satisfying constraint (13) on the derivative of the corresponding cumulative distribution functions & first parameter used in the analytical procedure for deriving BTA lower bounds ' second parameter used in the analytical procedure for deriving BTA lower bounds ( ) derivative of a generic function () with respect to variable ()| ̅ value of a generic function () evaluated at = ̅ * #th BTA value + # minimum among all the real values contained in a generic discrete set , , , - discrete set with values , , , - discrete set with all values for indices # from 1 to " random variable representing the maximum values of the link input traffic observed with a given time granularity random variable representing the minimum values of the link input traffic observed with a given time granularity random variable representing the average values of the link input traffic observed with a given time granularity (!) cumulative distribution function of the average link input traffic observed with a time granularity on the order of 1 second . total number of bits transmitted over a backhaul link in a given time interval / number of seconds in a generic time interval 0 1 average throughput exchange (in bit/s) that is expected during busy hours over a given backhaul link 2 coefficient representing the portion of the day classified as "busy hours" 2 coefficient representing the anticipated traffic reduction during off-peak periods with respect to busy hours 2 coefficient expressing the ratio between the expected peak traffic ! and the average traffic demand [] for a given backhaul link th radio site of a generic backhaul network 3 total number of possible radio configurations utilized in a link planning example 4() metric value for the th radio link configuration () maximum transmit power (in dBm) of the radio equipment available in the th radio link configuration 5 () transmit antenna gain (in dB) of the radio equipment available in the th radio link configuration ETSI ETSI TR 104 141 V1.1.1 (2026-03) 12 5 () receive antenna gain (in dB) of the radio equipment available in the th radio link configuration () PIR fade margin (in dB) guaranteed by the th radio link configuration transmit power relative to the PIR (in dBm) of the radio equipment available in the th radio link configuration 6 free-space path loss (in dB) experienced over a given backhaul link 56 attenuation due to atmospheric gases (in dB) experienced over a given backhaul link / () receiver sensitivity threshold relative to the PIR (in dBm) of the radio equipment available in the th radio link configuration , maximum capacity delivered by a given backhaul link 7 coefficient expressing the ratio between the actual peak traffic value ! and the maximum capacity , delivered by a given backhaul link 5!"! overall antenna gain in dB (namely, including both the receive and the transmit side) of a given backhaul link distance covered by a given backhaul link ℓ,#5!"! actual link BTA obtained for the ℓth traffic time series and the link distance , considering an overall antenna gain equal to 5!"! 6 total number of time series included in the th test dataset 8 cardinality of the set of pairs of parameters (&, ') used in the numerical analysis (& , ' ) th choice of a pre-defined set of 8 pairs of parameters (&, ') 6ℓ,#, (5!"!) BTA lower bound obtained for the ℓth traffic time series, the link distance and the th pair (& , ' ) to be used in constraint (13), considering an overall antenna gain equal to 5!"! 9ℓ,#, relative error between the BTA lower bound and the actual link BTA obtained for the ℓth time series, the link distance and the th pair (& , ' ) to be used in constraint (13) Δℓ,#, excess gain in dB needed to achieve a BTA lower bound 6ℓ,#, (5!"!) numerically equal to the actual link BTA ℓ,#(5!"!), obtained for the ℓth time series, the link distance and the th pair (& , ' ) to be used in constraint (13) : lower bound efficiency, defined as the percentage of cases in which the proposed analytical procedure employing the th pair (& , ' ) in constraint (13) succeeds in generating actual BTA lower bounds ;() step function evaluated at the argument < total cost of ownership of a target backhaul network when the traditional planning methodology is applied number of resolvable intervals of the link lengths distribution of a target backhaul network = cost of the least expensive transport technology that can be employed to cover all the connection distances included in the th interval while satisfying the target conditions of the traditional planning methodology relative number of links in a target backhaul network with distances included in the th interval <$ % total cost of ownership of a target backhaul network when the New KPIs planning methodology is applied =̃ cost of the least expensive transport technology that can be employed to cover all the connection distances included in the th interval while satisfying the target conditions of the New KPIs planning methodology ETSI ETSI TR 104 141 V1.1.1 (2026-03) 13 ℒ #th subset of links of a given backhaul network generic aggregation node in a given backhaul network ? target end-to-end BTA to be guaranteed for the traffic generated by a generic radio site of a given backhaul network @@@@@@ target BTA to be guaranteed over the #th link of a given backhaul network A number of radio sites in a generic backhaul network |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 3.3 Abbreviations | For the purposes of the present document, the following abbreviations apply: 2G 2nd Generation 3G 3rd Generation 4G 4th Generation 5G 5th Generation ACM Adaptive Coding and Modulation ATTM Access, Terminals, Transmission and Multiplexing BH Backhaul BTA Backhaul Traffic Availability CDF Cumulative Distribution Function CIR Committed Information Rate DL Downlink ETSI European Telecommunications Standards Institute FDD Frequency Division Duplex FWA Fixed Wireless Access GBR Guaranteed Bit Rate KPI Key Performance Indicator LTE Long Term Evolution MIMO Multiple-Input-Multiple-Output MNO Mobile Network Operator mWT millimetre Wave Transmission PAR Peak-to-Average Ratio PIR Peak Information Rate QoE Quality of Experience RAN Radio Access Network RAT Radio Access Technology RTPC Remote Transmit Power Control SLA Service Level Agreement TCO Total Cost of Ownership TDD Time Division Duplex UL Uplink 4 An analytical procedure for deriving BTA lower bounds |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 4.1 Overview | The aim of this clause is to present a quick and straightforward analytical procedure for deriving the lower bound of the BTA of any given backhaul link on the basis of the sole knowledge of its expected average and peak aggregated traffic demands. The proposed approach offers thus the key benefit of leading to the derivation of a worst-case (i.e. minimum achievable) BTA value that can be readily employed for a conservative and effective planning whenever an estimation of the complete statistical distribution of the expected link traffic demand is not available. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 14 The disclosed analytical procedure builds upon the possibility to reliably capture the statistical model of the traffic demand of any transport link through a conveniently selected Beta probability distribution, as debated in [i.1]. An overview of the use of the Beta distributions family to model backhaul traffic demand dynamics is given in clause 4.2, while the method for deriving conservative BTA values is detailed in clause 4.3. 4.2 Modelling the aggregated traffic demand of backhaul links through Beta distributions The Beta distribution is a family of parametric continuous probability distributions defined on the interval [0,1]. The analytical expressions of the probability density function , , = &'1 −(' ΓΓ Γ + , (2) the cumulative distribution function , , = , , , (3) and the expected value = + (4) of a generic Beta-distributed random variable depend on the value of the two positive parameters and , being Γ = B )''* + (5) the gamma function, and , , = Γ + ΓΓ × B &'1 −(' (6) the regularized incomplete Beta function with parameters and [i.3], with ∈[0,1]. Previous studies conducted by the ETSI ATTM TM_mWT community [i.1] argue that the statistical behaviour of the random variable representing the traffic demand of any backhaul link can be accurately modelled by a properly re-scaled Beta distribution. This implies that, based on relations (2), (3), and (4), the probability density function, the cumulative distribution function and the expected value of any traffic demand variable with maximum (peak) value ! can be approximated as: !, , = 1 ! × C ! ! , , D = !&'! −!(' ! &,('ΓΓ Γ + , (7) !, , = C ! ! , , D, (8) and = + ! , (9) respectively, for a convenient choice of the shape parameters and , and with the independent variable ! ∈[0, ! ]. NOTE 1: Throughout the present document, the traffic carried over any link at any given time instant is modelled as a random variable characterized by a statistical distribution that is assumed to remain constant over time. NOTE 2: Terms maximum and peak are used interchangeably within the present document when referring to traffic demand random variables. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 15 Figure 3: Illustrative probability density functions and cumulative distribution functions of Beta-distributed traffic demand random variables with fixed Figure 4: Illustrative probability density functions and cumulative distribution functions of Beta-distributed traffic demand random variables with fixed ETSI ETSI TR 104 141 V1.1.1 (2026-03) 16 Figure 5: Illustrative probability density functions and cumulative distribution functions of Beta-distributed traffic demand random variables with Figures 3, 4 and 5 show illustrative probability density functions and cumulative distribution functions of Beta-distributed traffic demand random variables with maximum value = 2 Gbit/s, for different choices of the parameters and , according to equations (7) and (8). It is remarked that some combinations of and can lead to unrealistic traffic demand distribution shapes where high capacities tend to become more probable than values in the low-to-medium range (for example, for 0,5 in figure 4-(a) and 0,5 in figure 5-(a)). These preliminary graphical considerations will be further expanded and elaborated in the following clause 4.3 and annex A devoted to the disclosure of the analytical procedure for deriving BTA lower bounds. |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 4.3 The analytical procedure for deriving BTA lower bounds | The possibility of using the Beta distributions family to approximate the statistical behaviour of any backhaul traffic demand allows to derive the analytical method disclosed in the present clause, targeted at associating any transport link with a worst-case BTA value that can be readily used for a conservative but effective planning. According to equation (9), the ensemble of traffic demand random variables characterized by a maximum value and an average value "#$ can be statistically described by a continuous subspace ℳ of the re-scaled Beta distributions family where parameters and are related according to the following rule: 1 & & , (10) being & "#$/ (11) the normalized average traffic demand value. By way of example, the coloured area in the chart of figure 6 illustrates the ensemble of the re-scaled Beta cumulative distribution functions belonging to the continuous subspace ℳ ,, that can be used to represent the statistical behaviour of any traffic demand random variable with normalized average value & "#$/ 0,3. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 17 Figure 6: Ensemble of the re-scaled Beta cumulative distribution functions )*, , belonging to the continuous subspace + , Once fixed the backhaul technology, which determines the ordered set of transport capacities , - (with . . . ) and the corresponding availabilities , - (the latter obtainable through the application of the well-established guidelines and methods in Recommendation ITU-R P.530-19 [i.4]), the ensemble of BTA values associated with all the cumulative distribution functions belonging to the continuous subspace ℳ lie within a closed interval for any value of the normalized average traffic demand &, as sketched in figure 7 for the illustrative case of a 3,4 km E-band backhaul connection employing 500 MHz bandwidth (therein, a peak traffic demand value = 3 100 Mbit/s is assumed). This consideration is at the basis of the analytical procedure described in table 1, that aims, for an input pair of average and peak expected traffic demand values, at finding the BTA lower bound (i.e. the BTA worst-case) of any link under investigation by conducting a convenient search on the space of , parameters satisfying equation (10). NOTE: In this figure, the set of transport capacities and the corresponding availabilities characterizing a 3,4 km E-band connection operating with a 500 MHz bandwidth have been utilized for deriving the ensemble of BTA values associated with all the traffic cumulative distribution functions belonging to the continuous subspaces ℳ , for different values of the normalized average demand on the abscissa (f = 0,1, 0,2, …, 0,9), with = 3 100 Mbit/s. Figure 7: Ensemble of BTA values associated with all the traffic cumulative distribution functions belonging to the continuous subspaces +, for different values of . ETSI ETSI TR 104 141 V1.1.1 (2026-03) 18 Table 1: Analytical procedure for deriving the BTA lower bound of any given backhaul link Initialization 1) Determine the set of transport capacities E ()F and the corresponding availabilities E ()F of the backhaul link of interest (with () < () < . . . < ()); 2) define a discrete set $ = ,- of " possible values for the shape parameter , with > 10'; 3) compute the normalized average traffic demand value according to equation (11), where: 3a) denotes the average value of the expected traffic demand over an entire year, or, if unavailable, over any time period not shorter than one full day (24 hours); 3b) ! denotes the maximum value of the expected traffic demand that can be theoretically generated by the ensemble of the RAT layers transported over the backhaul link under analysis, and it can be readily computed according to the well-established guidelines in, e.g., NGMN 0.4.2 [i.2]; 4) for each value ∈$, compute the corresponding shape parameter according to relation (10) as: = ' ; (12) 5) create a discrete set % that contains all the pairs (, ) satisfying the following constraint on the derivative of the corresponding cumulative distribution functions !, , evaluated at ! = & × ! [bit/s]: G!, , G! H !-×! = !, , |!-×! = 1 ! × &&'1 −&(' ΓΓ Γ + ≤ ' ! , (13) with & = 0,999 and = 0,05 (see annex A for a detailed explanation), and being , , the re-scaled Beta probability density function defined in equation (7); BTA lower bound computation 6) for each pair of (, ) parameters belonging to the set derived in step 5: 6a) compute the probabilities that the link traffic demand falls in the adjacent intervals (), () = 1,2, … , as: < ≤ = , , − , , , (14) being = 0 bit/s the link failure state, and , , = , , (15) the re-scaled Beta cumulative distribution function with shape parameters (, ), according to equation (8); 6b) estimate the corresponding BTA value according to equation (1); 7) return as the worst-case BTA value (i.e. the BTA lower bound) for the link under consideration. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 19 It is remarked that, while steps 1 through 4 lead to the creation of a discretized version of the continuous subspace ℳ (for the selected value of ), the additional constraint (13) has the role of further limiting the search space over the two parameters , in order to improve the tightness of the lower bound with respect to the distribution of all the possible BTAs that can realistically occur in practical scenarios. More specifically, inequality (13), imposing that the derivative of the cumulative distribution functions , , evaluated at is lower than 0,05/ (with 0,999), sets a restriction on the maximum slope of the admissible CDFs in the high-traffic region (see figure 8 for a graphical explanation). This leads to the final effect of including in the search process of the BTA lower bounds only the shape parameters , associated with distributions where high traffic demands occur with progressively smaller probabilities with respect to lower capacities, thus excluding, by way of example, the unrealistic behaviours previously outlined in figures 4 and 5 (i.e. for 0,5 in figure 4-(a) and 0,5 in figure 5-(a)). For illustration, the ensemble of the re-scaled Beta cumulative distribution functions , , corresponding to traffic demand random variables with normalized average value / 0,3 that satisfy inequality (13) is plotted in figure 9. NOTE: This figure shows that, according to constraint (13), the local slopes , and of the cumulative distribution functions , , , , , and , , of illustrative traffic demand random variables, respectively, evaluated at should be ≤ /. Figure 8: A graphical representation of constraint (13) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 20 Figure 9: Ensemble of the re-scaled Beta cumulative distribution functions , , corresponding to traffic demand random variables with normalized average value / , that satisfy inequality (13) The analytical expression of constraint (13), along with the choice for parameters and , has been defined on the basis of the results of an extensive simulation campaign conducted on a database of realistic traffic time series collected from live backhaul networks (see annex A for a detailed description of the employed methodology). The computational complexity of the whole analytical procedure illustrated in table 1 scales linearly with the cardinality of the initial set ! of parameters . The totality of the numerical results and evaluations based on the dataset of traffic time series described in annex A suggests that values of on the order of a few hundred are sufficient to optimize the intrinsic trade-off between lower bound accuracy and computational complexity of the proposed method. Consequently, the disclosed procedure is expected to be easily integrable into currently commercialized software planning tools with minimal resource demands. 5 Traffic demand distribution models through measurement campaigns on live networks |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 5.1 Overview | The definition of appropriate prediction models of the backhaul traffic demand statistics can lead to the strategic benefit of making the choice of the throughput distributions required for computing the BTA metric a process completely transparent to the end user, and can thus greatly contribute to the successful adoption of the New KPIs methodology. In this context, the availability of realistic measurements collected from operative networks is recognized as crucial to enable the definition first and the validation then of effective traffic distribution forecast strategies. The present clause 5 is devoted to the presentation of a methodology for conducting measurement campaigns on live transport networks with the ultimate goals of: 1) obtaining a proprietary database of cumulative distribution functions of the traffic demand experienced by a statistically relevant set of backhaul links deployed in different environmental (e.g. in terms of topology and subscribers' density) and technological (e.g. in terms of transported RAN configurations) conditions; 2) grouping the obtained traffic demand distributions into homogeneous clusters of links with similar features; ETSI ETSI TR 104 141 V1.1.1 (2026-03) 21 3) describing each cluster of links through a compact set of representative CDFs of the traffic demand (e.g. in terms of the 5th percentile lower-bound, the 95th percentile upper-bound and the median cumulative distribution functions as better explained in the following) to be subsequently used as references in the planning phase of any network according to the New KPIs methodology. General guidelines for the collection, classification and clustering of traffic data will be provided in clauses 5.2, 5.3, and 5.4, respectively, while a discussion on the experimental results obtained during the preparation of the present document will be matter of clause 5.5. |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 5.2 Data collection | To assess the novel BTA metric (defined in equation (1)), ETSI GR mWT 028 [i.1] recommends to employ the cumulative distribution function of the average input traffic demand of the link observed with a time granularity on the order of 1 second. The present clause is devoted to discuss practical guidelines to achieve an estimate of this latter link feature by solely relying on the performance monitoring systems already available in the currently deployed wireless transport networks. As a first observation, it is remarked that the input traffic demand of a given backhaul link - theoretically definable as the amount of pure end-to-end throughput unaffected by any bottleneck possibly introduced by the link itself - represents a quantity that is inherently difficult - if not impossible - to measure directly. As a practical and reasonable approximation, the input traffic demand can be assumed to correspond to the input traffic of the backhaul link under consideration measured at the ingress port (see figure 10 for a schematic representation), provided that the link operates under (virtually) ideal propagation and deployment conditions to avoid any possible capacity reductions or congestion effects (such as those caused by fading-related outages). Figure 10: Recommended measurement points for the input traffic flowing over the backhaul link under consideration in both the downlink and the uplink directions Secondly, performance monitoring systems implemented in currently deployed backhaul networks typically provide aggregated measurements of the input traffic of each link (such as the maximum, the minimum and the average values) with a time granularity on the order of minutes (e.g. 15 minutes), which makes the collection of raw throughput data with higher sampling rates (e.g. with a temporal resolution on the order of 1 second as specified in ETSI GR mWT 028 [i.1]) not achievable. It is further observed that, although monitoring systems are continuously evolving and will soon enable the measurement of per-link input traffic dynamics at time granularities as fine as one second, opting for coarser resolutions will always remain a viable choice - particularly in contexts where minimizing the signalling overhead constitutes a priority. Based on these practical considerations, effective measurement campaigns should be aimed at inspecting a statistically relevant number of wireless transport links and at obtaining, for each of them: • a cumulative distribution function of the random variable representing the maximum values of the input traffic observed with the lowest possible time granularity (usually on the order of minutes in current networks), being the independent variable expressed in bit/s; • a cumulative distribution function of the random variable representing the minimum values of the input traffic observed with the lowest possible time granularity; ETSI ETSI TR 104 141 V1.1.1 (2026-03) 22 • a cumulative distribution function of the random variable representing the average values of the input traffic observed with the lowest possible time granularity. Since, though, input traffic values are typically sampled at the transmit devices with intrinsic integration times (and, therefore, granularities) of few seconds - before being aggregated into wider time periods for the use of current performance monitoring systems -, it is here emphasized that, for any backhaul link, the cumulative distribution function of the average input traffic observed with a time granularity on the order of 1 second as specified in ETSI GR mWT 028 [i.1]: i) is upper-bounded and lower-bounded by and , respectively; and ii) is quite accurately represented by in the median region, as sketched in figure 11. Therefore, computing the BTA of any link through equation (1) by employing cumulative distribution function will always produce a more pessimistic value compared to the one based on . Figure 11: Schematic representation of the cumulative distribution functions , , and Tighter approximations to the actual cumulative distribution function of the average input traffic observed with a time granularity on the order of 1 second could be achieved by linearly combining the aggregated throughput measurements , and with coarser resolutions, or the corresponding functions , and defined before. Activities aimed at defining the preferred methodology for this purpose should rely on validations using traffic data sampled at intervals on the order of a few seconds as ground truth. A step in this direction has been made during the preparation of the present document by recording for 1 week the traffic dynamics - with a time granularity of 1 second - of two tail links (namely, providing backhaul connectivity to single multi-band LTE RAN sites) and two feeder links (aggregating three and four LTE RAN sites respectively) deployed in both urban and rural areas of an EU Country. Afterwards, the derived datasets have been processed in order to derive, for each link: • the cumulative distribution function of the originally measured traffic time series with 1 second time granularity; • the cumulative distribution functions , and of the random variables , and representing the maximum, minimum and average traffic values over adjacent 15-minute time windows, respectively; • the cumulative distribution function obtained by averaging and ; • the cumulative distribution function of the random variable " /2. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 23 The test outcomes are summarized in figure 12, illustrating that, in all cases, the CDF of the random variable " /2 provides the closest approximation to the target function . Consequently, this CDF should also be considered as a relevant output of the data collection phase for each link. Specifically, the results of the experimental activity described in clause 5.5 will be entirely based on this latter statistical distribution. Figure 12: Cumulative distribution functions of the average input traffic measured with a time granularity on the order of 1 second compared with the cumulative distribution functions obtained on the basis of throughput data aggregated over adjacent 15-minute time windows |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 5.3 Data classification | Each traffic cumulative distribution function obtained by applying the guidelines described in clause 5.2 should be also associated with a list of significant attributes (also referred to as labels in the following) characterizing the link under analysis, e.g. including the deployment conditions and the involved RAN technologies and configurations. The factors with the highest impact on the traffic statistical behaviour and that should be primarily considered in the link classification process are expected to belong to the following macro-categories: 1) Location of the transported RAN sites. 2) Type of the transported RATs. 3) Number of radio layers for each transported RAT. 4) Number of cells per site, utilized bandwidth, duplex mode and MIMO configuration for each transported radio layer. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 24 Further details and insights about these attributes are discussed in the following. 1) Location of the transported RAN sites RAN sites with the same technological configurations can generate different amounts of traffic, depending on the deployment area and the users' distribution. Moreover, a precise classification of the site location can provide important indications about the future RAN traffic growth, that in turn can be used in the elaboration of accurate throughput demand prediction models. Sites deployed in urban or dense-urban areas are characterized by higher loads than rural locations and could be of stronger interest for mobile operators as they can generate more revenues. 2) Type of the transported RATs The data streams flowing over the currently deployed backhaul links typically originate from a mix of 2G, 3G, 4G, and 5G RATs. However, since 4G and 5G layers are generally the primary contributors to the overall exchanged throughput, focusing exclusively on these latter technologies can greatly streamline the classification process without, on the other hand, compromising the validity of the analysis. Accordingly, the focus should be restricted to identify only the following possible categories: • 4G-only sites, with or without Fixed Wireless Access (FWA) users • 5G-only (stand-alone) sites, with or without FWA users • Dual 4G/5G sites, with or without FWA users The FWA attribute is currently seen by the industry as a crucial traffic booster in 5G deployments, and it is therefore expected to play a key role in the determination of the traffic patterns. 3) Number of radio layers for each transported RAT Even within the same geographical region, different RAN sites can vary significantly in the number and configuration of 4G and 5G technologies. LTE sites deployed in urban or dense-urban areas are typically equipped with: • an underlying layer ensuring coverage, usually operating in the lowest available frequency bands (700 MHz, 800 MHz, or 900 MHz), with a bandwidth of 10 MHz or 15 MHz (most commonly 10 MHz) and utilizing 2x2 MIMO base stations; • one or two additional layers in higher frequency bands (1 800 MHz, 2 100 MHz, 2 300 MHz, or 2 600 MHz), designed to boost capacity in densely populated areas. These layers usually operate with a bandwidth of 20 MHz or multiples thereof and rely on 4x4 MIMO base stations. The same pattern of coverage and additional high-capacity layers is also expected for mature 5G installations. Table 2 complements this description by providing an example of different RAN site configurations (from column six to eight) deployed in the same urban area of an existing 4G and 5G mobile network. Table 2: Example of different RAN site configurations (from column six to eight) deployed in the same urban area of an existing 4G and 5G mobile network RAN technology Bandwidth [MHz] Duplex Technology MIMO Number of cells Configuration 1 Configuration 2 Configuration 3 4G 800 MHz 10 FDD 2x2 3 x x x 4G 1 800 MHz 20 FDD 2x2 3 x 4G 2 100 MHz 15 FDD 2x2 3 x 4G 1 800 MHz 20 FDD 4x4 3 x 4G 2 100 MHz 15 FDD 4x4 3 x 5G 2 600 MHz 90 TDD 4x4 3 x 5G 3 500 MHz 100 TDD 4x4 3 x x ETSI ETSI TR 104 141 V1.1.1 (2026-03) 25 4) Number of cells per site, utilized bandwidth, duplex mode and MIMO configuration for each transported radio layer Each radio access technology layer can provide connectivity to various cells per site (typically three or four in urban or dense-urban scenarios, but even up to six in specific cases), with different duplex modes (e.g. TDD or FDD) and MIMO configurations (2x2 or 4x4 MIMO for 4G, and massive MIMO for 5G), leading to an enormous number of possible combinations. To simplify the overall data collection and classification process, measurement campaigns should focus on the most probable configurations, and proper scaling laws could be defined in order to predict the relevant statistical properties of traffic in less frequent scenarios. Based on the most relevant macro-categories described above, a reference list of labels that could be used for classifying the measured backhaul links is provided in table 3, that also includes implementation-oriented indications that can foster future software integration. Additionally, the illustrative values reported in the last column have been derived by considering an exemplary backhaul link aggregating traffic from two radio sites deployed in a densely populated and non-touristic urban area: • the first site is configured with two 4G layers (with 10 MHz and 15 MHz bandwidth, respectively), both carrying mobile-only traffic via 2x2 MIMO FDD base stations, and one 5G layer (100 MHz bandwidth), also carrying mobile-only traffic via 64x64 massive MIMO TDD base stations with a DL:UL ratio of 80:20; • the second site hosts three 4G layers with 20 MHz, 15 MHz and 20 MHz bandwidth, respectively, all carrying both mobile and FWA traffic through 4x4 MIMO FDD base stations. In the considered example, all RATs have been assumed to operate with three sectors. Table 3: Reference list of labels for traffic data classification Type No. Label Values Unit Example Site 1 N Aggregated Sites Integer (1 in case of Tail link, >1 in case of Feeder link) 2 2 Coverage Type list of letters ∊ [ D (Dense-Urban), U (Urban), S (Sub-Urban), R (Rural) ] with length "N Aggregated Sites" D D 3 Area Type list of letters ∊ [ T (Touristic), N (Non Touristic) ] with length "N Aggregated Sites" N N 4G 4 4G RAT Layers list of integers with length "N Aggregated Sites" 2 3 5 4G Bandwidth list of integers with length "sum(4G RAT Layers)" [MHz] 10, 15 20, 15, 20 6 4G MIMO Layers Tx list of integers with length "sum(4G RAT Layers)" 2, 2 4, 4, 4 7 4G MIMO Layers Rx list of integers with length "sum(4G RAT Layers)" 2, 2 4, 4, 4 8 4G Duplex Mode list of strings ∊ [ FDD (for FDD), TDD_90, TDD_80, TDD_70, ..., TDD_10 (for TDD with DL:UL ratio equal to 90:10, 80:20, 70:30, ..., 10:90, respectively) ] with length "sum(4G RAT Layers)" FDD, FDD FDD, FDD, FDD 9 4G Sectors list of integers with length "sum(4G RAT Layers)" 3, 3 3, 3, 3 10 4G Service Type list of strings ∊ [ NN, NY, YN, YY ] with length "sum(4G RAT Layers)" (see note below) NN, NN YN, YN, YN 5G 11 5G RAT Layers list of integers with length "N Aggregated Sites" 1 0 12 5G Bandwidth list of integers with length "sum(5G RAT Layers)" [MHz] 100 13 5G MIMO Layers Tx list of integers with length "sum(5G RAT Layers)" 64 14 5G MIMO Layers Rx list of integers with length "sum(5G RAT Layers)" 64 15 5G Duplex Mode list of strings ∊ [ FDD (for FDD), TDD_90, TDD_80, TDD_70, ..., TDD_10 (for TDD with DL:UL ratio equal to 90:10, 80:20, 70:30, ..., 10:90, respectively) ] with length "sum(5G RAT Layers)" TDD_80 16 5G Sectors list of integers with length "sum(5G RAT Layers)" 3 17 5G Service Type list of strings ∊ [ NN, NY, YN, YY ] with length "sum(5G RAT Layers)" (see note below) NN ETSI ETSI TR 104 141 V1.1.1 (2026-03) 26 |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 5.4 Clustering of traffic demand distributions | The ensemble of measured and properly classified traffic CDFs should be then assigned to a reduced set of homogeneous clusters on the basis of their respective link attributes. Afterwards, a group of representative cumulative distribution functions should be identified for each cluster, such as (see figure 13 for reference): • the median CDF, the 95th percentile upper-bound CDF (excluding 5 % of CDFs with higher values), and the 5th percentile lower-bound CDF (excluding 5 % of CDFs with lower values) of the cluster of cumulative distribution functions of the random variables representing the maximum values of the link input traffic observed with the lowest possible time granularity (e.g. 15 minutes); • the median CDF, the 95th percentile upper-bound CDF, and the 5th percentile lower-bound CDF of the cluster of cumulative distribution functions of the random variables " /2, where and represent the maximum and minimum traffic values, respectively, of the link input traffic observed with the lowest possible time granularity (e.g. 15 minutes). Figure 13: Schematic representation of the median CDF, the 95th percentile upper-bound CDF and the 5th percentile lower-bound CDF of a given cluster of cumulative distribution functions Any subsequent planning process according to the New KPIs methodology should be then built upon: i) the association of the link under analysis with one of the derived clusters (on the basis of its attributes); and ii) the assessment of the BTA through equation (1) by using one of the six representative CDFs mentioned above. A recommended strategy is to base the link design on the choice of the 5th percentile lower-bound CDF of the random variables " /2 as a mildly conservative scenario. Alternatively, the 5th percentile lower-bound CDF of the random variables can be adopted in case a more precautionary approach is preferred. In addition, each cluster of CDFs may be further processed to derive reference average and maximum traffic values to be used as inputs for the application of the analytical procedure for deriving BTA lower bounds as described in clause 4.3. Type No. Label Values Unit Example NOTE: First letter in each string of labels 10 and 17 in table 3 is Y (=Yes) if site carries FWA traffic, while it is N (=No) otherwise; second letter is Y (=Yes) if site carries Enterprise traffic, while it is N (=No) otherwise. Mobile traffic is assumed present by default. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 27 |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 5.5 Experimental results | The present clause 5.5 illustrates the results of a measurement campaign conducted with the primary goal of providing a guideline on how to apply the methodologies described in clauses 5.2 through 5.4, which are targeted to identify the representative traffic statistical distributions of a set of reference RAN scenarios to be directly utilized in the BTA assessment. For this purpose, a total of 535 wireless backhaul links deployed in rural and sub-urban areas of a European Country have been monitored for the entire month of January 2024. All links provide connectivity to single three-sector RAN sites equipped with 4G (in all cases) and possibly 5G technology (in nearly 50 % of cases). As for the 4G layer, the communication in each sector is ensured by 2x2 MIMO base stations employing a total bandwidth ranging from 10 MHz to 80 MHz across several combinations of one-to-six frequency bands, all operated in FDD mode. When present, the 5G layer is implemented in the C-Band and utilizes base stations equipped with 64 transmit and 64 receive antennas. An indication on whether the different sites are deployed in touristic areas and deliver FWA services is also available (see table 4 and table 5 for more detailed statistics on the investigated links). Table 4: Distribution of the total 4G bandwidth utilized by the RAN sites reached by the backhaul links under analysis Total RAN sites RAN sites equipped with 5G RAN sites in touristic areas RAN sites providing FWA services 10 MHz 12 0 0 0 15 MHz 4 0 0 0 20 MHz 6 0 2 0 25 MHz 79 20 5 17 30 MHz 32 10 4 10 35 MHz 18 3 2 2 40 MHz 1 0 0 0 45 MHz 190 89 24 82 50 MHz 3 0 2 0 60 MHz 161 108 36 97 80 MHz 29 16 9 17 Total 535 246 84 225 Table 5: Correlation among RAN sites equipped with 5G technology, sites deployed in touristic areas and sites providing FWA services 5G Touristic FWA 5G 246 27 223 Touristic 27 84 23 FWA 223 23 225 During the experimental campaign, the bidirectional time series of the maximum, average and minimum percent bandwidth utilization within the one-month observation period have been extracted and recorded for each link with a time granularity of 15 minutes by leveraging the available performance monitoring systems. Then, the measured bandwidth utilization time series have been converted into traffic time series - in bit/s - by considering the actual interface port speed of the link equipment in each 15-minute time window. For every link, only the communication direction with the highest transported capacities (carrying downlink radio access flows) has been accounted for in the analysis presented in this clause. Afterwards, the selected traffic time series of each link have been used to derive the relevant CDFs as detailed in clause 5.2, which in turn have been accurately classified by applying the guidelines in clause 5.3. The successive clustering phase has been based on the only cumulative distribution functions of the random variables ( + )/2 of each link, obtained by averaging the minimum and the maximum throughput values measured at 15-minute intervals, as the best approximation to the CDFs of the traffic observed with a time granularity on the order of 1 second recommended for computing the BTA according to ETSI GR mWT 028 [i.1] (see clause 5.2). Furthermore, the analysis has been restricted to the three largest 4G bandwidth categories identified in table 4 - namely 25 MHz, 45 MHz, and 60 MHz - thereby narrowing the considered dataset to a subset of 430 links out of the original 535. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 28 Grouping the selected traffic time series on the basis of the total amount of 4G bandwidth leads to the three clusters of cumulative distribution functions illustrated in figures 14-(a), 14-(b) and 14-(c), where only the median CDF, the 95th percentile upper-bound CDF and the 5th percentile lower-bound CDF are drawn for each case, according to the guidelines in clause 5.4. Despite the fact that there are significant differences in the statistical behaviour of the traffic produced by the three bandwidths - as outlined by figure 14-(d) that summarizes the 50th percentile and the 99th percentile values of the median CDF of each group -, the resulting three clusters of cumulative distribution functions mostly overlap (as shown in figure 15, where all lines in figures 14-(a), 14-(b) and 14-(c) are superimposed in the same plot). Figure 14: Representative CDFs and reference values of the three clusters of cumulative distribution functions derived by employing the only total 4G bandwidth information Figure 15: Superposition of the median CDF, the 95th percentile upper-bound CDF and the 5th percentile lower-bound CDF of each of the three clusters in figure 14 ETSI ETSI TR 104 141 V1.1.1 (2026-03) 29 A further level of grouping of the traffic time series based on a second attribute becomes therefore necessary to obtain more distinguishable clusters with a reduced spread between the 95th percentile upper-bound and the 5th percentile lower-bound CDFs. Among all the available link information, the "Touristic" label has not been considered due to both the limited number of surveyed sites with this feature and the observation period (falling in the off-peak season), while both the 5G and FWA attributes have been investigated separately (although almost all 5G sites also provide FWA services, as reported in table 5) obtaining, e.g., figure 16 for the case of 25 MHz total 4G bandwidth (similar outcomes have been achieved for 45 MHz and 60 MHz). The plots suggest that a further grouping of the traffic time series either on the basis of the presence or absence of 5G technologies in the connected sites (figure 16-(a)), or on the basis of the presence or absence of FWA services (figure 16-(b)), represents an effective strategy for decreasing the spread of the identified clusters, thus confirming, as expected, the key role of the 5G and FWA dimensions in the clustering process. Unfortunately, the strong correlation among sites equipped with 5G radio access technology and those offering FWA services does not allow to appreciate the mutually exclusive use of these two important labels. Therefore, the second clustering dimension of the present study can only be formulated as "links carrying 5G and/or FWA traffic". The considerations expressed in the present clause, even if related to a limited number of surveyed links, provide a first example of how to apply the methodology described in clauses 5.2 through 5.4, and corroborate the importance of some of the key clustering attributes identified in clause 5.3. NOTE: This figure shows the 95th percentile upper-bound CDFs and 5th percentile lower-bound CDFs of the clusters of traffic time series with or without 5G data streams (in black and red colour, respectively, in (a)), and with or without FWA services (in black and red colour, respectively, in (b)) for the only case of 25 MHz total 4G bandwidth. As a reference, the lower-bound and upper-bound CDFs of the overall cluster with a total of 25 MHz 4G bandwidth (i.e. considering the first classification attribute only) are also shown in both (a) and (b) in grey. Figure 16: Clusters of traffic time series with or without 5G data streams and with or without FWA services for the case of 25 MHz total 4G bandwidth |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 6 Link planning example | |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 6.1 Overview | The present clause 6 is entirely devoted to the description of a comprehensive planning procedure based on the New KPIs methodology, with specific reference to the illustrative backhaul scenario sketched in figure 17. Before delving into the link design example, clause 6.2 explores practical strategies for determining both the average and the peak values of the expected traffic demand of any backhaul link of interest, that are key parameters for the application of the analytical procedure for deriving BTA lower bounds, as outlined in clause 4. Afterwards, clause 6.3 gets to the heart of the case study by describing the main system and link assumptions, followed by clauses 6.4 and 6.5 which illustrate the planning workflow by applying the methods for computing the BTA metric detailed in the previous parts of the present document. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 30 6.2 Derivation of the average and the peak values of the expected link traffic demand As highlighted in clause 4, the analytical procedure for deriving BTA lower bounds relies on the availability of accurate estimates of both the average traffic demand [ ] and the peak traffic demand expected over the transport link under consideration. Specifically: • denotes the average value of the expected traffic demand over an entire year, or, if unavailable, over any period not shorter than one full day (24 hours); • denotes the maximum value of the expected traffic demand that can be theoretically generated by the ensemble of the transported RAT layers. While well-defined guidelines exist for computing the peak throughput values (e.g. as outlined in NGMN 0.4.2 [i.2] and illustrated in figure 1 for the illustrative case of a RAN site with three sectors), there is currently no standardized approach for estimating the average traffic demands. In practice, the derivation of the latter quantities is often carried out using proprietary tools developed by mobile network operators or vendors, whose internal mechanisms and specifications fall outside the scope of the present document. Nevertheless, to ensure a self-contained and accessible methodology, a few approaches that have been recognized as valid during the preparation of the present document are outlined below. Alternative strategies - either derived from those listed here or complementary to them - may also be adopted. 1) Determination from empirical measurements The average traffic demand value [ ] (in bit/s) for the link of interest can be estimated by measuring the total number of bits transmitted over a transport connection with similar characteristics (e.g. in terms of the attributes illustrated in clause 5) during a time interval of seconds (with ≥86 400), and then dividing the observed quantity by , as: = (16) 2) Determination from busy hours average traffic The average traffic demand value [] (in bit/s) for the link of interest can be estimated by appropriately scaling the average throughput exchange [ ] (in bit/s) that is expected during busy hours according to the following expression: = + 1 − , (17) where coefficient r₁ (≤ 1) represents the portion of the day classified as "busy hours", and r₂ (≤ 1) reflects the anticipated traffic reduction during off-peak periods. EXAMPLE: Assuming 8 hours of busy time per day and a 50 % reduction of the average traffic during off-peak hours with respect to , it would yield = 8/24 = 1/3 and = 1/2. In this case, the average traffic value [ ] during busy hours can be obtained either through simulation or by direct measurement of transport connections that exhibit similar characteristics (e.g. in terms of the attributes described in clause 5) to the link of interest. NOTE: Throughout the present document, the expression busy hours denotes the specific time period during the day when a network or system experiences its highest volume of activity or traffic. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 31 3) Determination from the peak traffic value The average traffic demand value (in bit/s) for the link of interest can be estimated as a fraction of the expected peak traffic (in bit/s) as: , (18) where the scaling factor can be chosen in the range 3,8 when the target backhaul link connects a single RAN site. For a conservative network planning, a value of 3 should be selected. |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 6.3 Scenario description | Clause 6 pursues the task of planning a bidirectional backhaul connection between two radio sites at a distance of 5,3 km and deployed in a geographical region where rainfall intensities exceed 32 mm/h for 0,01 % of the time in a year (see figure 17 for reference). The expected traffic volume to be transported across the two communication paths is unbalanced, and it is characterized by a peak load = 3,5 Gbit/s and an average value = 1,36 Gbit/s in the direction with the highest demand - both of which are known parameters. Figure 17: Overview of the illustrative backhaul scenario considered in clause 6, with information on the link distance, the rain zone and the main traffic statistical parameters Based on this objective, the selected radio equipment consists of a single-polarization transceiver unit operating in the E-band, featuring a bandwidth of 500 MHz and a maximum output power of 26 dBm, with a maximum gross information rate equal to 4,5 Gbit/s yielding a net throughput greater than the expected peak traffic load . The unit supports an Adaptive Coding and Modulation (ACM) policy, and it is therefore capable of dynamically adjusting the delivered capacities according to the fading conditions along the propagation path. In this scenario, two types of parabolic antennas are assumed to be available: one with a diameter of 30 cm (maximum gain of 45,4 dBi), and another with a diameter of 60 cm (maximum gain of 51,4 dBi). These give rise to three possible radio link configurations: • Configuration 1: one E-band unit equipped with 30 cm antenna at both the radio sites; • Configuration 2: one E-band unit with 30 cm antenna at the first site and one E-band unit with 60 cm antenna at the second site; • Configuration 3: one E-band unit with 60 cm antenna at both the radio sites. The purpose of clause 6 is to define the optimal radio link configuration to ensure compliance with the following target conditions, derived from a New-KPIs-oriented link planning methodology (see Introduction for further details): • CIR = 25 Mbit/s available for at least 99,995 % of the time ETSI ETSI TR 104 141 V1.1.1 (2026-03) 32 • BTA higher than 99,97 % • Gross PIR = 4,5 Gbit/s with at least 5 dB of fade margin It is observed that the BTA constraint expressed above can be interpreted, for instance, as stemming from the application of the apportionment rule outlined in annex A of the ETSI GR mWT 028 [i.1] to the network topology depicted in figure 18, where three radio sites - , and - are connected to a remote aggregation node in a daisy-chain configuration (see annex C for further details on the recommended guidelines for BTA apportionment in generalized network scenarios). Specifically, the selection of the target BTA values for each link illustrated in the diagram ensures that the traffic generated by each of the three radio sites achieves an overall end-to-end BTA (i.e. up to node ) that consistently meets or exceeds the threshold of 99,9 % recommended in [i.1]. Figure 18: Illustrative BTA apportionment for a network topology with three radio sites connected to a remote aggregation point in a daisy-chain configuration Two distinct link planning procedures are outlined in the following, depending on whether an estimate of the complete statistical distribution of the expected traffic demand, in addition to the provided peak and average values ( and , respectively), is available or not. |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 6.4 Link planning with known traffic distribution | When an estimate of the cumulative distribution function of the link's target traffic demand is available - such as from the employment of the measurement-based methodology described in clause 5 - the planning procedure should follow the general steps specified in table 6. Table 6: Link planning procedure according to the New KPIs methodology with known cumulative distribution function of the link's target traffic demand Initialization 1) For each th radio link configuration, compute the following metric: (19) as the sum of the maximum transmit power (in dBm), the transmit antenna gain (in dB) and the receive antenna gain (in dB) of the available radio equipment; 2) sort the available radio link configurations in ascending order based on the metric , to obtain: 1 2 ⋯ , (20) being the total number of radio link configurations; 3) initialize 1; Link Planning 4) compute the metrics prescribed by the New KPIs methodology considering the th radio link configuration: 4a) derive the PIR fade margin as: , (21) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 33 where is the transmit power of the radio equipment relative to the PIR (in dBm), an are defined as in step 1, is the free-space path loss (in dB), is the attenuation due to atmospheric gases (in dB), and () is the receiver sensitivity threshold relative to the PIR (in dBm); NOTE: Term can be computed according to Recommendation ITU-R P.676-13 [i.5]. 4b) derive the availability () of each th backhaul capacity (with () < () < . . . < ()) according to the well-established methodologies based on the fading prediction models described in Recommendation ITU-R P.530-19 [i.4]; 4c) for all indexes = 1,2, … , , compute the probability < ≤ that the link throughput demand lies in the range between backhaul capacities and on the basis of the target traffic cumulative distribution function (assumed known in the present clause) as: < ≤ = −( ), (22) being = 0 bit/s the link failure state; 4d) derive the BTA through the formula presented in equation (1), which is reported here for ease of reference and improved readability: = ( < ≤ ) × () , (23) where terms () and ( () < ≤ ()) (for = 1,2, … ) have been obtained in previous steps 4b and 4c, respectively; 5) if the PIR fade margin (), the BTA and the CIR availability derived in step 4 meet the planning targets, then end the procedure and plan the link with the th radio configuration, otherwise increment the counter by 1; 6) if ≤ repeat the process starting from step 4, otherwise end the procedure and conclude that no radio link configuration among the available ones can meet the planning targets. NOTE: The initialization phase presented in table 6 (and in following table 8) serves solely as an illustrative example. Its purpose is to organize the candidate radio link configurations in a logical sequence, in order to begin with the simplest (i.e. least costly) option and to progressively test more complex (or expensive) solutions in the subsequent Link Planning steps 4, 5 and 6, until all New KPIs target conditions are satisfied. Depending on the specific context, alternative and more sophisticated sorting criteria can be necessary to accommodate varying strategic needs. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 34 Figure 19: Cumulative distribution function of the link's target traffic demand used to derive the BTA values in table 7 through equations (22) and (23) Table 7 shows the values of the PIR fade margin , the BTA ! and the CIR availability that can be obtained by applying steps 4a through 4d of the methodology in table 6 to each th radio link configuration defined in clause 6.3 (here 1,2,3), assuming that the cumulative distribution function of the link's target traffic demand is the one depicted in figure 19. Based on the reported outcomes, it can be easily deduced that the planning procedure of table 6 would result in the selection of the second radio link configuration ( 2), that delivers the minimum system gain needed to satisfy all the New KPIs target conditions specified in clause 6.3. To provide a broader perspective, the results presented in table 7 are complemented by the plot in figure 20, that illustrates the variation of: i) the PIR fade margin (right y-axis); ii) the BTA calculated according to equation (23) on the basis of the link's target traffic demand distribution shown in figure 19 (left y-axis); iii) the lower bound of the BTA derived through the analytical procedure described in clause 4.3 (which forms the core of the approach discussed in the following clause 6.5) (left y-axis); and iv) the CIR availability (left y-axis) as a function of the combined transmission and reception antenna gains. As a reference, the gain levels ensured by radio link configurations 1, 2 and 3, corresponding to 90,8 dBi, 96,8 dBi and 102,8 dBi, respectively, are also highlighted on the x-axis. Table 7: Key results obtained by applying steps 4a through 4d of the planning procedure illustrated in table 6 to the radio link configurations described in clause 6.3 Radio link configuration PIR fade margin BTA CIR availability 1 9,26 dB 99,964 % 99,995 % 2 15,28 dB 99,978 % 99,997 % 3 21,30 dB 99,986 % 99,998 % ETSI ETSI TR 104 141 V1.1.1 (2026-03) 35 Figure 20: Variation of the metrics used in the New KPIs planning methodology as a function of the combined transmission and reception antenna gains It is remarked that, while the example discussed in the present clause 6 involves selecting only the size of the antennas to complete the link design process, other planning scenarios could be more complex and involve candidate radio configurations with also variations in the total output powers provided at the antenna port, arising, for instance, from the availability of different product versions or the implementation of Remote Transmit Power Control (RTPC) policies - which ultimately result in limitations on the effective radiated power and are thus sensitive to the employed transmission antenna gains. Nevertheless, the approach outlined in table 6 can be easily extended to account for these additional cases. |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 6.5 Link planning with unknown traffic distribution | When the information on the link's target traffic demand distribution is not available, the BTA computation should be based on the conservative but effective approach described in clause 4.3. Table 8 illustrates all the steps needed to plan any backhaul link according to the New KPIs methodology in this case. Table 8: Link planning procedure according to the New KPIs methodology with unknown cumulative distribution function of the link's target traffic demand Initialization 1) For each th radio link configuration, compute the following metric: (24) as the sum of the maximum transmit power (in dBm), the transmit antenna gain (in dB) and the receive antenna gain (in dB) of the available radio equipment; 2) sort the available radio link configurations in ascending order based on the metric , to obtain: 1 2 ⋯ , (25) being the total number of radio link configurations; 3) initialize 1; ETSI ETSI TR 104 141 V1.1.1 (2026-03) 36 Link Planning 4) compute the metrics prescribed by the New KPIs methodology considering the th radio link configuration: 4a) derive the PIR fade margin ( ) as: ( ) = + + ( ) − − − ( ), (26) where is the transmit power of the radio equipment relative to the PIR (in dBm), an are defined as in step 1, is the free-space path loss (in dB), is the attenuation due to atmospheric gases (in dB), and ( ) is the receiver sensitivity threshold relative to the PIR (in dBm); NOTE: Term can be computed according to Recommendation ITU-R P.676-13 [i.5]. 4b) derive the availability ( ) of each th backhaul capacity (with ( ) < ( ) < . . . < ( )) according to the well-established methodologies in Recommendation ITU-R P.530-19 [i.4]; 4c) derive the BTA lower bound by following the analytical procedure detailed in clause 4.3 (table 1), using as inputs the current availabilities ( ) (computed in step 4b) and the average and the peak values of the expected traffic demand [] and , respectively; 5) if the PIR fade margin ( ), the BTA lower bound and the CIR availability derived in step 4 meet the planning targets, then end the procedure and plan the link with the th radio configuration, otherwise increment the counter by 1; 6) If ≤ repeat the process starting from step 4, otherwise end the procedure and conclude that no radio link configuration among the available ones can meet the planning targets. Table 9 reports the values of the PIR fade margin ( ), the BTA lower bound and the CIR availability that can be derived by applying steps 4a through 4c of the methodology in table 8 to each th radio link configuration. Comparing these results with the New KPIs target conditions specified in clause 6.3 clearly indicates that, also in this case, the overall planning procedure in table 8 would designate configuration 2 as the preferred radio technology for the link under study. Table 9: Key results obtained by applying steps 4a through 4c of the planning procedure illustrated in table 8 to the radio configurations described in clause 6.3 Radio link configuration PIR fade margin () BTA lower bound CIR availability = 1 9,26 dB 99,955 % 99,995 % = 2 15,28 dB 99,974 % 99,997 % = 3 21,30 dB 99,984 % 99,998 % For the sake of clearness, the results obtained from the application of the analytical method of clause 4.3 are here reported for the only case of configuration 1 (first row of table 9). Specifically, table 10 shows the set of the test parameters selected in the present example (as per step 2 of the procedure in table 1), the corresponding parameters derived from equation (12), the indication on whether constraint (13) is satisfied or not, and the resulting BTAs . In the examined scenario, the analytical method would yield a BTA lower bound equal to 99,955 %, which corresponds to the minimum value among the BTAs displayed in the fourth column of table 10 that satisfy condition (13). ETSI ETSI TR 104 141 V1.1.1 (2026-03) 37 Table 10: Results obtained from the application of the analytical method proposed in clause 4.3 to the radio link configuration 1 ∈ from equation (12) Is constraint (13) satisfied? BTA [%] 0,05 0,079 No 99,891 0,1 0,157 No 99,902 0,2 0,315 No 99,918 0,3 0,472 No 99,929 0,4 0,629 No 99,937 0,5 0,786 No 99,942 0,6 0,944 No 99,946 0,7 1,101 No 99,949 0,8 1,258 No 99,952 0,9 1,415 No 99,953 1 1,573 Yes 99,955 2 3,145 Yes 99,961 3 4,718 Yes 99,963 4 6,290 Yes 99,963 5 7,863 Yes 99,964 10 15,726 Yes 99,965 20 31,451 Yes 99,966 40 62,902 Yes 99,967 60 94,353 Yes 99,967 62 97,498 Yes 99,967 64 100,643 Yes 99,967 66 103,788 Yes 99,967 68 106,934 No 99,967 70 110,079 No 99,967 |
314690db30e0da820145d5cd1eef3bc5 | 104 141 | 7 Conclusions | Planning backhaul networks through the New KPIs methodology calls for reliable techniques for predicting the traffic demand distributions across the different links, which are essential for accurately evaluating the novel BTA metric. The present document has introduced two complementary solutions to address this challenge. The first method relies on an analytical procedure designed to estimate the worst-case BTA of any backhaul link. Its key advantage is that it is based solely on the knowledge of the average and peak values of the expected link traffic demand, rather than the full throughput statistical distribution. Although this approach inherently yields a conservative BTA evaluation, an experimental validation activity based on traffic time series collected from operational wireless transport networks has demonstrated its effectiveness in significantly reducing link over-engineering compared to current planning criteria. The second method involves conducting measurement campaigns on live backhaul systems with the aim of deriving a dataset of traffic demand distributions to be possibly used as reference statistics for the assessment of the BTA in various deployment scenarios, with different environmental (e.g. in terms of topology and subscribers' density) and technological (e.g. in terms of transported RAN configurations) conditions. It is worth noting that these two strategies can also be employed in a synergistic manner. In some cases, the data collected in real radio fixed networks could provide valuable insights into the traffic loads which may be expected in specific contexts of interest, that can in turn be used as inputs to the analytical procedure for computing worst-case (or lower bound) BTA values. The contributions described in the present document - aimed at defining practical methodologies for assessing the BTA metric - are believed to represent a significant advancement in promoting and accelerating the adoption of the New KPIs-based design paradigm in current and future wireless backhaul networks. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 38 Annex A: Experimental validation of the analytical procedure for deriving BTA lower bounds: methodology and results A.1 Overview Annex A provides a detailed description of the methodology used to define constraint (13) in the analytical procedure for deriving BTA lower bounds as illustrated in table 1. As already mentioned in clause 4.3, inequality (13) limits the search of the BTA lower bounds within the subspace of (, ) parameters that generate plausible traffic demand distributions where high throughput values occur with progressively smaller probabilities with respect to lower capacities, thus excluding, by way of example, the unrealistic behaviours previously highlighted in figures 4 and 5 (for = 0,5 in figure 4-(a) and = = 0,5 in figure 5-(a)). More specifically, this condition has been imposed by setting a restriction on the slope of the admissible cumulative distribution functions in the high-traffic region, and, in mathematical terms, it has been achieved by requiring that the maximum value of the derivative of the CDF , , produced by a generic pair of parameters (, ) and evaluated at a traffic load equal to × (say with 0,9 ≤ ≤1) is upper-bounded by a convenient term /: ,, × = = × , , = × () Γ + ≤ , (A.1) where , , is the probability density function associated with the pair of parameters (, ), and is given by equation (7). The choice = 0,999 and = 0,05 for constraint (13) has been determined as a result of a thorough simulation campaign based on a dataset of traffic time series acquired from a currently operative backhaul network, as further detailed below. The remainder of annex A is organized as follows. Clause A.2 provides an overview of the employed database, while clause A.3 describes the methodology adopted for the simulation activity, in terms of main assumptions, utilized metrics and test cases. Finally, clause A.4 discusses the numerical results. A.2 Database description The results described in annex A are based on a database composed of 1 510 time series obtained by recording the peak traffic loads of a set of links deployed in a commercial backhaul network for an overall period of 18 weeks, with a time resolution of 1 hour. It is remarked that this coarse data resolution (e.g. with respect to the 15 minutes time granularity typically supported by performance monitoring systems in currently deployed backhaul networks) does not constitute a limitation for the following analysis, since the real purpose of annex A is to numerically (and massively) assess how effectively the proposed analytical procedure is able to derive accurate lower bounds on the BTAs of transport links with arbitrary - yet realistic - traffic distributions, rather than to achieve a New-KPIs-compliant link planning (a process that would have undoubtedly involved managing traffic data with time resolutions on the order of 1 second, according to ETSI GR mWT 028 [i.1] and as discussed in different occasions within the present document). In order to prevent the present study from being biased in favour of traffic distributions that would not be realistically and practically considered in any rationale backhaul link design process, the time series composing the above-mentioned database have been selected so as to: • have a Peak-to-Average Ratio (PAR) lower than or equal to 8; • have analytically continuous cumulative distribution functions; • have cumulative distribution functions with probabilities higher than 99 % and 99,8 % for traffic values greater than 0,99 × and 0,998 × (being the maximum value of each time series), respectively, thus ensuring a smooth behaviour in the high-throughput region (figure A.1 illustrates graphically this condition). ETSI ETSI TR 104 141 V1.1.1 (2026-03) 39 NOTE: Only the time series characterized by cumulative distribution functions with probabilities higher than 99 % and 99,8 % for traffic values greater than 0,99 and 0,998 , respectively, have been included in the database. Figure A.1: Graphical representation of the condition imposed on the cumulative distribution functions of the selected time series A.3 Methodology, system assumptions and test cases Each time series in the database described in clause A.2 has been re-scaled so as to have: ,, (A.2) where parameter 1 accounts for the unavoidable gap between the actual peak traffic value and the maximum capacity , that can be delivered by the employed radio transport technology, and it will be varied in the different test cases considered in the present study. Backhaul links operating in E-band (at around 80 GHz) over a bandwidth of 500 MHz with vertically polarized electromagnetic field and supporting an ACM policy are here considered as the reference transport technology. More specifically, the analysis illustrated in clause A.4 will focus on five representative links, with hop lengths 1, 2, 3, 4 and 5 km, all deployed in a region with rainfall intensities exceeding 42 mm/h for 0,01 % of the time in a year. A total of nine test datasets have been derived from the original database described in clause A.2: Test dataset I: composed of all the traffic time series (i.e. with PAR ≤ 8), and selecting 0,82 in equation (A.2); Test dataset II: composed of all the traffic time series, and selecting 0,9; Test dataset III: composed of all the traffic time series, and selecting 0,95; Test dataset IV: composed of the only traffic time series with PAR ≤ 6, and selecting 0,82; Test dataset V: composed of the only traffic time series with PAR ≤ 6, and selecting 0,9; Test dataset VI: composed of the only traffic time series with PAR ≤ 6, and selecting 0,95; Test dataset VII: composed of the only traffic time series with PAR ≤ 4, and selecting 0,82; Test dataset VIII: composed of the only traffic time series with PAR ≤ 4, and selecting 0,9; ETSI ETSI TR 104 141 V1.1.1 (2026-03) 40 Test dataset IX: composed of the only traffic time series with PAR ≤ 4, and selecting = 0,95. For each th test dataset ( = I, II, III, IV, V, VI, VII, VIII, IX): 1) a cumulative distribution function has been first computed for each ℓth traffic time series, and has been then employed to generate, by applying equation (1), the actual link BTA value for each considered hop length : ℓ, (ℓ= 1,2, … , ; = 1, 2, 3, 4, 5 km), (A.3) where dependence on the overall antenna gain (namely, including both the receive and the transmit side), in dB, is explicitly indicated to facilitate the following description, while is the total number of time series included in the th test dataset. NOTE 1: To derive the actual link BTAs, the availabilities of the different capacities offered by the radio connection for each hop length have been computed on the basis of Recommendation ITU- R P.530-19 [i.4] by taking into account free-space loss, gaseous absorption and rain attenuation as the primary propagation impairments. 2) an average and a maximum traffic load value ([] and , respectively) have been derived for each ℓth traffic time series in the dataset, and have been employed as inputs to the analytical procedure as detailed in table 1, that has been run for each link distance and for each th choice of a pre-defined set of pairs of parameters ( , ) to be used in constraint (13), in order to derive the corresponding BTA lower bound: ℓ,,( ) (ℓ= 1,2, … , ; = 1, 2, 3, 4, 5 km; = 1,2, … ). (A.4) The analysis presented in clause A.4 will evaluate all the combinations of ∈ 0,99, 0,999, 0,9999, 0,99999 and ∈ 0,002, 0,004, 0,006, 0,008, 0,01, 0,02, 0,03, 0,04, 0,05, 0,06, 0,07, 0,08, 0,09, 0,1, 0,2, 0,22, 0,24, 0,26, 0,28, 0,3, 0,32, 0,34, 0,36, 0,38, 0,4, 0,5, 0,6, 0,7, 0,8, 0,9, 1, leading to an overall cardinality = 4 × 31 = 124. 3) a relative error between the derived BTA lower bound (step 2) and the actual link BTA (step 1) has been then computed for each ℓth time series, for each link distance and for each th pair ( , ) as: ℓ,, = − (1 − ℓ,( )) −(1 − ℓ,, ) (1 − ℓ,( )) . (A.5) NOTE 2: In formula (A.5), the relative error is expressed in terms of the outage quantities (1 − ℓ,( )) and (1 − ℓ,,( )). 4) an excess gain Δℓ,, in dB needed to achieve a BTA lower bound ℓ,, numerically equal to the actual link BTA ℓ, has been computed for each ℓth time series, for each link distance and for each th pair ( , ). In mathematical notation, excess gain Δℓ,, guarantees that: ℓ,, + Δℓ,, = ℓ, . (A.6) It is highlighted that the latter metric has the scope of quantifying, in [dB], the superfluous system margin that the employment of the proposed analytical procedure for deriving BTA lower bounds with parameters ( , ) would introduce in a possible planning process involving a link with length and carrying a traffic expressed by the ℓth time series. In clause A.4, the best pair of parameters ( , ) is investigated for every th test dataset on the basis of an assessment of the following three key metrics: 1) the statistical distribution of the relative errors ℓ,, derived for all the time series and the five link distances under consideration; 2) the statistical distribution of the excess gains Δℓ,, derived for all the time series and the five link distances under consideration; ETSI ETSI TR 104 141 V1.1.1 (2026-03) 41 3) the lower bound efficiency , defined as the percentage of cases in which the proposed analytical procedure employing the th pair ( , ) in constraint (13) succeeds in generating actual BTA lower bounds. Since, according to equation (A.5), ℓ,,( ) constitutes a lower bound on the actual link BTA value computed for each ℓth time series and for each link distance if and only if ℓ,, ≥0, it yields: = 1 × 5 ℓ,, × 100 [%], ℓ (A.7) where () is the step function, defined as: = 1 ≥0 0 < 0 (A.8) A.4 Numerical results The numerical results obtained for Test datasets I through IX are summarized in tables A.2 through A.10, respectively, which show, for each th choice of the pair of parameters ( , ) (i.e. for each th row): i) the lower bound efficiency (third column); ii) the minimum relative error experienced over all the time series and the five link distances (fourth column); and iii) the median (fifth column), the 75th percentile (sixth column), the 90th percentile (seventh column), the 95th percentile (eighth column) and the 99th percentile (ninth column) of the statistical distribution of the excess gains Δℓ,, derived for all the time series and the five link distances (i.e. ℓ= 1,2, … , and = 1,2,3,4,5 km). It is observed that, according to equation (A.5), the minimum relative error in the fourth column is negative whenever the efficiency metric is strictly lower than 100 %, and in those cases it provides an important indication of the extent to which an actual link BTA value can fall below the BTA lower bound computed according to the proposed method with the th pair ( , ) used in constraint (13). NOTE: For conciseness, tables A.2 through A.10 only report a sub-selection of the analysed values of parameters ( , ). Tables A.2 through A.10 illustrate that employing the analytical procedure with relaxed conditions on constraint (13) (for example, in rows 3-4 of table A.2) guarantees to find actual BTA lower bounds for all the links ( = 100 %), at the expense of a generally high excess gain (larger than 5 dB in 50 % of cases in the considered examples). At the same time, the results suggest that the inherent trade-off between lower bound tightness and overall accuracy can be optimized by pursuing the reasonable compromise of selecting pairs of parameters ( , ) that could cause a slight degradation in the lower bound efficiency (with respect to the full scale of 100 %), while yielding the advantage of maintaining the excess gain below 3 dB in the majority (i.e. in the range 95 % to 99 %) of cases. It is remarked that the 3 dB value is compatible with the margin that is typically considered in network planning phases to compensate for unexpected system losses along the transmission links, such as those caused by misalignments between transmit and receive antennas due to imperfect installation. Accordingly, in the present document the optimum pair ( , ) is defined as the one that results in the minimum 95th percentile value of the excess gain across all test cases, while simultaneously guaranteeing the following two conditions: C1 lower bound efficiency always higher than 99 %; C2 minimum relative error experienced in all test cases higher than -21 % (it is recalled that the relative error is negative only when the BTA lower bound value is higher than the actual link BTA, according to equation (A.5)). It is noted that conditions C1 and C2 imply that the proposed analytical procedure can fail to compute an actual lower bound in only 1 % of the cases within the considered datasets, and that, in such rare circumstances, the BTA lower bound can exceed the actual link BTA by only a very limited amount. To illustrate this quantitatively, table A.1 reports, for each BTA lower bound value shown in the first column, the minimum actual link BTA that could result according to condition C2 and equation (A.5). ETSI ETSI TR 104 141 V1.1.1 (2026-03) 42 Table A.1: BTA lower bounds and corresponding minimum actual link BTAs according to condition C2 BTA lower bound [%] Minimum actual link BTA [%] 99,9 99,873418 99,95 99,936709 99,99 99,987342 99,995 99,993671 99,999 99,998734 Considering all the analysed test cases, the optimum pair of parameters , that minimizes the 95th percentile value of the excess gain and that at the same time guarantees conditions C1 and C2 turns out to be: 0,999, 0,05, (A.9) and this is the recommended choice for the application of the analytical procedure in table 1 of clause 4.3. Table A.2: Selected results for Test dataset I (PAR ≤ 8, ω = 0,82) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 43 Table A.3: Selected results for Test dataset II (PAR ≤ 8, ω = 0,9) Table A.4: Selected results for Test dataset III (PAR ≤ 8, ω = 0,95) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 44 Table A.5: Selected results for Test dataset IV (PAR ≤ 6, ω = 0,82) Table A.6: Selected results for Test dataset V (PAR ≤ 6, ω = 0,9) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 45 Table A.7: Selected results for Test dataset VI (PAR ≤ 6, ω = 0,95) Table A.8: Selected results for Test dataset VII (PAR ≤ 4, ω = 0,82) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 46 Table A.9: Selected results for Test dataset VIII (PAR ≤ 4, ω = 0,9) Table A.10: Selected results for Test dataset IX (PAR ≤ 4, ω = 0,95) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 47 Annex B: A methodology for analysing the impacts of New KPIs on Total Cost of Ownership The aim of the present annex B is to illustrate a methodology for obtaining a general assessment of the benefits, in terms of cost of ownership, brought by the New KPIs paradigm. The envisioned approach starts by identifying a target backhaul network and a desired peak capacity that needs to be transported over each link (for the sake of simplicity - and without affecting the validity of the presented methodology - a unique peak capacity value for all the links is here considered). Based on these inputs, the cost savings enabled by the New KPIs planning methodology - with respect to traditional designs - can be evaluated according to the following step-by-step procedure: 1) derive the statistical distribution of the link lengths of the target backhaul network with a predefined granularity, e.g. obtaining a relative frequency histogram qualitatively similar to the one shown in figure B.1 (therein with 1 km granularity); NOTE: This figure illustrates the percentages of links spanning a distance that falls within each th interval (for 1, 2, … , 12). Figure B.1: Qualitative representation of the relative frequency histogram of link lengths in the target backhaul network 2) identify a set of preferred transport technologies that are able to deliver the desired peak capacities; 3) define the target conditions for both the traditional and the New KPIs planning methodologies. By way of example, table B.1 shows a possible set of objectives to be guaranteed for the two approaches; 4) for each transport technology as selected in step 2, identify the maximum link distances that can be achieved by employing both the traditional and the New KPIs planning methodologies; 5) compute the Total Cost of Ownership (TCO) of the target backhaul network planned according to the traditional planning methodology as: , (B.1) where index varies across the resolvable intervals of the link lengths distribution as derived in step 1 ( 12 in the example of figure B.1), is the cost of the least expensive transport technology that can be employed to cover all the connection distances included in the ith interval while satisfying the target conditions (e.g. those outlined in the first row of table B.1), while is the relative number of links in the network with distances included in the ith interval (see figure B.1 for a graphical representation); ETSI ETSI TR 104 141 V1.1.1 (2026-03) 48 6) similarly to step 5, compute the TCO of the target network planned according to the New KPIs methodology as: ̃ ; (B.2) 7) compare the TCOs derived at steps 5 and 6 to determine the cost savings enabled by the New KPIs planning methodology. NOTE: Within each th link lengths interval, the set of the eligible transport technologies depends on the maximum distance thresholds derived in step 4, therefore ̃ generally differs from parameter used in step 5. Figure B.2: Link lengths distribution of the backhaul network analysed as example in the present annex B Table B.1: Target conditions for the traditional and New KPIs planning methodologies considered in the present annex B Target conditions Traditional planning • ≥ 99,995 % availability for a capacity representing 15 % to 20 % of the desired peak capacity • ≥ 5 dB fade margin for the modulation format providing the desired peak capacity New KPIs planning • ≥ 99,995 % availability for a capacity at least equal to 200 Mbit/s • ≥ 99,9 % BTA • ≥ 5 dB fade margin for the modulation format providing the desired peak capacity For the sake of clarity, the remaining part of the present annex B will apply the procedure described above to assess the potential savings - in terms of costs related to the yearly spectrum license fees only - enabled by the New KPIs paradigm. Towards this goal, a hypothetical backhaul network characterized by a link lengths distribution as shown in figure B.2 is considered, and two target European deployment regions are taken into account, with rainfall intensities exceeding either 32 mm/h or 42 mm/h for 0,01 % of the time in a year (these will be referred to in the following as 32 mm/h and 42 mm/h rain intensity zones, respectively, for simplicity). Pursuing a desired peak capacity of 4 Gbit/s for all the connections, three backhaul solutions have been selected as candidate technologies: • an E-band point-to-point system with 250 MHz bandwidth and dual polarization; • a Dual Band point-to-point system operating on both E-band (250 MHz bandwidth, dual polarization) and 18 GHz frequency band (56 MHz bandwidth, dual polarization); ETSI ETSI TR 104 141 V1.1.1 (2026-03) 49 • an 18 GHz point-to-point system aggregating two 112 MHz channels and operating with dual polarization. Tables B.2 and B.3 quantify the maximum link lengths achievable by the three different transport technologies described above for 32 mm/h and 42 mm/h rain intensity zones, respectively, considering the target conditions shown in table B.1 (it is remarked that, in the present analysis, the assessment of the BTA has been based on the analytical procedure detailed in table 1 for all the links). Based on these results, the numerical evaluations that follow have been limited to the subset of links spanning a distance lower than or equal to 10,5 km and 10 km for the 32 mm/h and 42 mm/h rain intensity zones, respectively, which represent the largest achievable hop lengths under the traditional planning methodology in the two cases. The New KPIs, enabling a wider adoption of both the E-band and the Dual Band technologies compared to traditional planning approaches, position themselves as the most favourable metrics for reducing the overall spectrum-related expenditures in scenarios where yearly license fees are inversely proportional to the operational frequency - a characteristic that applies to the majority of cases in today's backhaul markets (by way of example, table B.4 reports the per-link annual spectrum costs for the three backhaul technologies under inspection in two European Countries). In line with this consideration, the present analysis has revealed significant savings of: • 22 % and 26 % in the 32 mm/h rain intensity zone when considering the per-link yearly spectrum license fees of EU Country 1 and 2 in table B.4, respectively; • 28 % and 35 % in the 42 mm/h rain intensity zone when considering the per-link yearly spectrum license fees of EU Country 1 and 2, respectively. It is also observed that, compared to backhaul technologies operating at lower frequencies, E-band systems require fewer radio channels to achieve the same delivered capacities, thanks to their significantly larger available bandwidths. Consequently, the adoption of the New KPIs approach - which extends the E-band usability range - offers the additional benefit of a more cost-effective deployment due to the generally reduced amount of hardware to be installed. Table B.2: Maximum achievable link lengths with traditional and New KPIs planning methodologies for 32 mm/h rain intensity zone Traditional planning New KPIs planning E-band 4,2 km 5,3 km Dual Band 8,8 km 14,3 km 18 GHz 10,5 km 10,5 km Table B.3: Maximum achievable link lengths with traditional and New KPIs planning methodologies for 42 mm/h rain intensity zone Traditional planning New KPIs planning E-band 3,3 km 4,2 km Dual Band 7 km 11,5 km 18 GHz 10 km 10,5 km Table B.4: Per-link annual spectrum license fees for the three backhaul technologies under inspection in two European Countries EU Country 1 EU Country 2 E-band 575 € 1 614 € Dual Band 1 955 € 4 924 € 18 GHz 3 850 € 12 841 € ETSI ETSI TR 104 141 V1.1.1 (2026-03) 50 Annex C: Method for determining the target BTAs on individual links in tree-shaped backhaul network topologies C.1 Method for a simple network topology Figure C.1: Backhaul network topology analysed in the present clause C.1 The present annex C focuses on the network topology illustrated in figure C.1, where four radio sites: , , , are connected to a common aggregation node via the distinct sets of links: ℒ 1, ℒ 2,1, ℒ 3,1, ℒ 4,3,1, respectively. It is assumed that the transport network under consideration needs to be planned to ensure that the traffic generated by each radio site ( 1,2,3,4) and directed to node achieves an end-to-end (i.e. over the whole cascade of connections belonging to set ℒ) BTA equal to . To meet these conditions, the target BTAs of the different backhaul links should satisfy the set of inequalities obtained by applying the BTA apportionment rule for daisy-chain topologies as described in annex A of ETSI GR mWT 028 [i.1] successively to all radio sites. More specifically, in the scenario of figure C.1: • radio site is connected to the aggregation node exclusively via link 1. Consequently, to meet the constraint on the objective end-to-end BTA , link 1 should be designed to achieve a target Backhaul Traffic Availability such that: 1 1 ; (C.1) • radio site reaches node through links 2 and 1. Therefore, the target BTAs for link 2 and link 1, denoted as and , respectively, should satisfy the following inequality: 1 1 1 ; (C.2) • radio site is connected to node via links 3 and 1. The corresponding target BTAs - and - should then satisfy: 1 1 1 ; (C.3) • radio site communicates with node through links 4, 3, and 1. The target BTAs for links 4, 3 and 1 - namely , and - should satisfy: 1 1 1 1 . (C.4) ETSI ETSI TR 104 141 V1.1.1 (2026-03) 51 Any selection of the target link BTAs - , , and - that satisfies the constraints in inequalities (C.1) through (C.4) ensures a valid network design capable of meeting the desired end-to-end Backhaul Traffic Availability levels , , and for the data flows generated by each node. When a uniform BTA apportionment is applied across the sequence of links connecting each radio site to the aggregation node , the inequalities (C.1) through (C.4) result in the following constraints on the individual target link BTAs : • for (single link): 1 − ≤ 1 − (C.5) • for (two links): 1 − ≤ 1 − 2 , 1 − ≤ 1 − 2 (C.6) • for (two links): 1 − ≤ 1 − 2 , 1 − ≤ 1 − 2 (C.7) • for (three links): 1 − ≤ 1 − 3 , 1 − ≤ 1 − 3 , 1 − ≤ 1 − 3 . (C.8) Based on constraints (C.5) through (C.8), the individual target link BTAs can be selected as follows: • for link 1, which is shared across all four radio sites, the most stringent constraint applies. Therefore: 1 − = 1 − , 1 − 2 , 1 − 2 , 1 − 3 (C.9) • for link 2, used only by radio site : 1 − = 1 − 2 (C.10) • for link 3, shared by sites and : 1 − = 1 − 2 , 1 − 3 (C.11) • for link 4, used exclusively by site : 1 − = 1 − 3 . (C.12) By applying equations (C.9) through (C.12), the network designer can determine the appropriate target BTAs for each individual link to ensure that the overall end-to-end Backhaul Traffic Availability conditions , , and are met for all the radio sites. Figure C.2 illustrates the resulting individual target BTAs for the case = = = = 99,9 %. ETSI ETSI TR 104 141 V1.1.1 (2026-03) 52 Figure C.2: Illustrative target BTAs for the individual links of the network topology in figure C.1 when = = = = 99,9 % and with uniform BTA apportionment strategy C.2 Method for all network topologies The method outlined in clause C.1 can be readily extended to an arbitrary transport network topology where a set of radio sites , , , … , are connected to a common aggregation point via cascades of multiple links, as illustrated in figure C.3. In this generalized scenario, a set of inequalities analogous to those derived for the four-site topology in figure C.1 can be obtained by systematically applying the BTA apportionment rule to every individual path from each radio site to the aggregation node , regardless of the number or arrangement of intermediate links. Figure C.3: Generalized transport network topology ETSI ETSI TR 104 141 V1.1.1 (2026-03) 53 History Version Date Status V1.1.1 March 2026 Publication |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 1 Scope | The present document provides a technical specification for an AI Common Incident Expression Framework for AI Incident Reporting. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 2 References | |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 2.1 Normative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. Referenced documents which are not found to be publicly available in the expected location might be found in the ETSI docbox. NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee their long-term validity. The following referenced documents are necessary for the application of the present document. [1] ETSI Collaborative tools for standardized technologies. [2] JSON Schema: "Specification". [3] IETF RFC 3986 (January 2005): "Uniform Resource Identifier (URI): Generic Syntax". |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 2.2 Informative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee their long-term validity. The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's understanding, but are not required for conformance to the present document. [i.1] ETSI TS 104 223 (V1.1.1): "Securing Artificial Intelligence (SAI); Baseline Cyber Security Requirements for AI Models and Systems". [i.2] Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act). [i.3] UK DSIT: "AI Cyber Security Code of Practice". [i.4] UK NCSC: "Guidelines for secure AI system development". [i.5] Directive (EU) 2022/2555 of the European Parliament and of the Council of 14 December 2022 on measures for a high common level of cybersecurity across the Union, amending Regulation (EU) No 910/2014 and Directive (EU) 2018/1972, and repealing Directive (EU) 2016/1148 (NIS 2 Directive). ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 7 [i.6] Commission Implementing Regulation (EU) 2024/2690 of 17 October 2024 laying down rules for the application of Directive (EU) 2022/2555 as regards technical and methodological requirements of cybersecurity risk-management measures and further specification of the cases in which an incident is considered to be significant with regard to DNS service providers, TLD name registries, cloud computing service providers, data centre service providers, content delivery network providers, managed service providers, managed security service providers, providers of online market places, of online search engines and of social networking services platforms, and trust service providers. [i.7] Directive (EU) 2022/2557 of the European Parliament and of the Council of 14 December 2022 on the resilience of critical entities and repealing Council Directive 2008/114/EC. [i.8] GCVE.EU: "GCVE: Global CVE Allocation System". [i.9] Cornell University SarXiv:2503.16861v1 [cs.AI], Sean McGregor et al.: "In-House Evaluation Is Not Enough: Towards Robust Third-Party Flaw Disclosure for General-Purpose AI", 21 March 2025. [i.10] OECD: "Towards a Common Reporting Framework for AI Incidents", OECD Artificial Intelligence Papers, February 2025, No. 34. [i.11] OECD: "AIM: AI Incidents and Hazards Monitor". [i.12] OECD: "Defining AI Incidents and Related Terms", OECD Artificial Intelligence Papers, May 2024, No. 16. [i.13] McGregor, S. (2021): "Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database", Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15458-15463. [i.14] OECD Framework for the Classification of AI Systems, February 2022, No. 323. [i.15] NCSC: "Where to Report a Cyber Incident". [i.16] NCSC: "Responding to a cyber incident - a guide for CEOs". [i.17] Confédération Suisse Federal Office for Cybersecurity BACS: "Information on the reporting obligation". [i.18] CSET: H. Frase & R.B. L. Dixon: "AI Incidents, Key Components for a Mandatory Reporting Regime", January 2025. [i.19] OWASP Gen AI Incident/Exploit Round-up Submission. [i.20] Recommendation ITU-T X.1500: "Overview of cybersecurity information exchange". [i.21] ETSI TR 104 003: "Cyber Security (CYBER); The vulnerability disclosure ecosystem". [i.22] ETSI TR 103 331: "Cyber Security (CYBER); Structured threat information sharing". [i.23] CISA: "Cybersecurity Incident & Vulnerability Response Playbooks". [i.24] NIST AI600-1: "Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile". [i.25] AIID: "AI Incident Database". [i.26] MIT: "MIT AI Risk Repository". [i.27] AVID: "AI Vulnerability Database". [i.28] OECD: "Overview and methodology of the AI Incidents and Hazards Monitor". [i.29] MIT: "MIT AI Risk Repository". [i.30] Kaggle: "AI Incident Database". ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 8 [i.31] JSON-LD.org: "JSON for Linking Data". [i.32] IEEETM CSR: Sharkov: "Unveiling the invisible: Knowledge Graph-Driven Discovery of Hidden Cascade Risks in Critical Infrastructure Supply Chains". [i.33] ETSI TS 104 158-2: "Securing Artificial Intelligence (SAI); AI Incident Reporting; Part 2: AI Common Incident Expression (AICIE) Common Container". |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 3 Definition of terms, symbols and abbreviations | |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 3.1 Terms | For the purposes of the present document, the following terms apply: Additional Data Publication (ADP): set of additional structured information that enriches existing AICIE records AI common incident expression identifier: alphanumeric string that uniquely identifies an AI Incident using the present document AI disaster: serious AI incident that disrupts the functioning of a community or a society and that may test or exceed its capacity to cope, using its own resources. The effect of an AI disaster can be immediate and localized, or widespread and lasting for a long period of time NOTE: More details available in [i.12]. AI hazard: event, circumstance or series of events where the development, use or malfunction of one or more AI systems could plausibly lead to an AI incident, i.e. any of the following harms: a) injury or harm to the health of a person or groups of people b) disruption of the management and operation of critical infrastructure c) violations to human rights or a breach of obligations under the applicable law intended to protect fundamental, labour and intellectual property rights d) harm to property, communities or the environment NOTE: More details available in [i.12]. incident: event, circumstance or series of events where the development, use or malfunction of one or more AI systems directly or indirectly leads to any of the following harms: a) injury or harm to the health of a person or groups of people b) disruption of the management and operation of critical infrastructure c) violations of human rights or a breach of obligations under the applicable law intended to protect fundamental, labour and intellectual property rights d) harm to property, communities or the environment NOTE: More details available in [i.12]. serious AI hazard: event, circumstance or series of events where the development, use or malfunction of one or more AI systems could plausibly lead to a serious AI incident or AI disaster, i.e. any of the following harms: a) the death of a person or serious harm to the health of a person or groups of people b) a serious and irreversible disruption of the management and operation of critical infrastructure c) a serious violation of human rights or a serious breach of obligations under the applicable law intended to protect fundamental, labour and intellectual property rights d) serious harm to property, communities or the environment ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 9 e) the disruption of the functioning of a community or a society and which may test or exceed its capacity to cope using its own resources NOTE: More details available in [i.12]. serious incident: incident or malfunctioning of an AI system that directly or indirectly leads to any of the following: a) the death of a person, or serious harm to a person's health b) a serious and irreversible disruption of the management or operation of critical infrastructure c) the infringement of obligations under Union law intended to protect fundamental rights d) serious harm to property or the environment NOTE: More details available in [i.2] and [i.12]. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 3.2 Symbols | Void. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 3.3 Abbreviations | For the purposes of the present document, the following abbreviations apply: AI Artificial Intelligence AICIE AI Common Incident Expression AIID AI Incident Database CSET Center for Security and Emerging Technology CVE Common Vulnerabilities and Exposures DSIT Department for Science, Innovation & Technology (UK) FAS Federation of American Scientists GCVE Global Common Vulnerabilities and Exposures NCSC National Cyber Security Centre (UK) (Switzerland) OECD Organisation for Economic Co-operation and Development URI Uniform Resource Identifier |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4 Existing Ecosystem | |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4.1 Cybersecurity information exchange models | The contemporary period of cybersecurity information exchange emerged in the 1990 timeframe and consisted of the exchange of combinations of incident, vulnerability, and threat information via repositories among a complex global community of product and service vendors, end users, and industry and government cyber security organizations. See [i.20], [i.21] and [i.22]. The actual sharing of information together with a broad array of related protocols consists of five activities. [i.20]: • structuring cybersecurity information for exchange purposes; • identifying and discovering cybersecurity information and entities; • establishment of trust and policy agreement between exchanging entities; • requesting and responding with cybersecurity information; • assuring the integrity of the cybersecurity information exchange. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 10 The incident response component has been formalized in cybersecurity models or playbooks and includes information sharing as part of the model. See [i.23]. See Figure 4.1-2. For AI specifically, disclosing incident information is included as part of 25 distinct GOVERN, MAP, MEASURE, and MANAGE tasks in the NIST AI Risk Management Framework. See [i.24]. Figure 4.1-2: Incident Response Process [i.23] |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4.2 AI incident information exchange implementations | During the past several years, several well-known, different AI incident repository implementations have emerged that provide different collections with varying record structures to serve a kind of emerging marketplace. See [i.11], [i.25], [i.26], [i.27], [i.28], [i.29] and [i.30]. The implementations lack a common discovery mechanism and interoperability. These insufficiencies combined with the emerging obligations articulated in clause 4.3, led to the OECD report on a common incident reporting and classification frameworks, see [i.10] and [i.14]. The rapid growth of AI implementations for all manner of use cases is certain is producing a rapidly expanding array of AI incident reporting needs and implementations. The situation makes compelling the need for a common framework for identifying, discovering and expressing incident information that is global, open, extensible, interoperable, scalable, and capable of accommodating the diversity of potential implementations. See [i.9], [i.13], [i.18] and [i.19]. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4.3 AI Incident information exchange obligations | |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4.3.1 The exchange obligation ecosystem | AI information exchange obligations arise somewhat autonomously from multiple sources. Organic legislative instruments and implementing regulations by regional and national authorities constitute obligations that incur penalties for non-compliance. See [i.4], [i.5], [i.6] and [i.7]. Government security agency and industry body technical standards constitute another source of implied obligation of "best practice" - sometimes coupled in juridical law with potential civil law liability exposure. See [i.15], [i.16] and [i.17]. The obligations can also arise by contractual agreement among parties in the provision of services. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 11 |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4.3.2 EU Artificial Intelligence Act | One of the most prominent contemporary legislative instruments is the EU Artificial Intelligence Act [i.2]. In five of its Articles, there are provisions that impose the below information sharing obligations. "Art. 17: Quality management system 1. Providers of high-risk AI systems shall put a quality management system in place that ensures compliance with this Regulation. … (i) procedures related to the reporting of a serious incident in accordance with Article 73; Art. 26(5): Obligations of deployers of high-risk AI systems … 5. Where deployers have identified a serious incident, they shall also immediately inform first the provider, and then the importer or distributor and the relevant market surveillance authorities of that incident. If the deployer is not able to reach the provider, Article 73 shall apply mutatis mutandis. Art. 60: Testing of high-risk AI systems in real world conditions outside AI regulatory sandboxes … 7. Any serious incident identified in the course of the testing in real world conditions shall be reported to the national market surveillance authority in accordance with Article 73. Section 2 - Sharing of information on serious incidents Art. 73: Reporting of serious incidents 1. Providers of high-risk AI systems placed on the Union market shall report any serious incident to the market surveillance authorities of the Member States where that incident occurred. 2. The report referred to in paragraph l shall be made immediately after the provider has established a causal link between the AI system and the serious incident or the reasonable likelihood of such a link, and, in any event, not later than 15 days after the provider or, where applicable, the deployer, becomes aware of the serious incident. The period for the reporting referred to in the first subparagraph shall take account of the severity of the serious incident. 3. Notwithstanding paragraph 2 of this Article, in the event of a widespread infringement or a serious incident as defined in Article 3, point (49)(b), the report referred to in paragraph 1 of this Article shall be provided immediately, and not later than two days after the provider or, where applicable, the deployer becomes aware of that incident. 4. Notwithstanding paragraph 2, in the event of the death of a person, the report shall be provided immediately after the provider or the deployer has established, or as soon as it suspects, a causal relationship between the high-risk AI system and the serious incident, but not later than 10 days after the date on which the provider or, where applicable, the deployer becomes aware of the serious incident. 5. Where necessary to ensure timely reporting, the provider or, where applicable, the deployer, may submit an initial report that is incomplete, followed by a complete report. 6. Following the reporting of a serious incident pursuant to paragraph 1, the provider shall, without delay, perform the necessary investigations in relation to the serious incident and the AI system concerned. This shall include a risk assessment of the incident, and corrective action. The provider shall cooperate with the competent authorities, and where relevant with the notified body concerned, during the investigations referred to in the first subparagraph, and shall not perform any investigation which involves altering the AI system concerned in a way which may affect any subsequent evaluation of the causes of the incident, prior to informing the competent authorities of such action. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 12 7. Upon receiving a notification related to a serious incident referred to in Article 3, point (49)(c), the relevant market surveillance authority shall inform the national public authorities or bodies referred to in Article 77(1). The Commission shall develop dedicated guidance to facilitate compliance with the obligations set out in paragraph 1 of this Article. That guidance shall be issued by 2 August 2025, and shall be assessed regularly. 8. The market surveillance authority shall take appropriate measures, as provided for in Article 19 of Regulation (EU) 2019/1020, within seven days from the date it received the notification referred to in paragraph 1 of this Article, and shall follow the notification procedures as provided in that Regulation. 9. For high-risk AI systems referred to in Annex ill that are placed on the market or put into service by providers that are subject to Union legislative instruments laying down reporting obligations equivalent to those set out in this Regulation, the notification of serious incidents shall be limited to those referred to in Article 3, point (49)(c). 10. For high-risk AI systems which are safety components of devices, or are themselves devices, covered by Regulations (EU) 2017/745 [Medical Device Regulation] and (EU) 2017/746 [In-vitro Medical Device Regulation], the notification of serious incidents shall be limited to those referred to in Article 3, point (49)(c) of this Regulation, and shall be made to the national competent authority chosen for that purpose by the Member States where the incident occurred. 11. National competent authorities shall immediately notify the Commission of any serious incident, whether or not they have taken action on it, in accordance with Article 20 of Regulation (EU) 2019/1020. Art. 76: Supervision of testing in real world conditions by market surveillance authorities … 3. Where a market surveillance authority has been informed by the prospective provider, the provider or any third party of a serious incident or has other grounds for considering that the conditions set out in Articles 60 and 61 are not met, it may take either of the following decisions on its territory, as appropriate: (a) to suspend or terminate the testing in real world conditions; (b) to require the provider or prospective provider and the deployer or prospective deployer to modify any aspect of the testing in real world conditions." |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 4.3.3 ETSI TS 104 223, UK DSIT Code of Practice | The United Kingdom's National Centre for Cyber Security (NCSC) developed a set of AI best practices [i.3] which were codified into regulator provisions by the regulatory body the Department for Science, Innovation and Technology (DSIT). The provisions were largely transposed into ETSI TS 104 223 [i.1] and contain five clauses impose information sharing obligations enumerated below: • Provision 5.1.2-3 To support the process of preparing data, security auditing and incident response for an AI system, Developers shall document and create an audit trail in relation to the AI system. This shall include the operation, and lifecycle management of models, datasets and prompts incorporated into the system. • Provision 5.2.2-5 Developers and System Operators shall create, test and maintain an AI system incident management plan and an AI system recovery plan. (DSIT Principle 6.4). • Provision 5.3.1-3 Developers and System Operators should support End-users and Affected Entities during and following a cyber security incident to contain and mitigate the impacts of an incident. The process for undertaking this should be documented and agreed in contracts with End-users. (DSIT Principle 10.2). • Provision 5.4.2-1 System Operators shall log system and user actions to support security compliance, incident investigations, and vulnerability remediation. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 13 |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5 AI Common Incident Expression (AICIE) Framework | |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.1 The AICIE Framework architecture | The present technical specification facilitates the AI information exchange ecosystem described in Clause 4, above, using a framework that establishes a design architecture for openness, diversity, extensibility, and interoperability among AI reporting communities. This global decentralised, autonomous framework architecture enables widespread sharing structured AI incident information via multiple common distributed directories that enable users to discover and access AI incident reporting resources in the form of other directory lists, specifications, incident repositories, or any other kind of related enrichment information. It is modelled after the Global CVE framework architecture. See [i.8]. The structure for the framework record format is set forth in Clause 5.2. The JSON representation is in Annex A. See [1], [2], [3] and [i.31]. An example of the ETSI implementation of the framework list is in Annex B. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2 AICIE Framework Resource Record Format and Values | |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.0 Introduction | The AICIE Framework resource record format is depicted in Figure 5.2-1 and captures the minimum essential information concerning an AI incident reporting resource. Bold field names are mandatory and "…" indicates free-form content. Resources include not only AICIE incident report containers described in other parts of the present document in repositories, but also any other existing or potential new AI incident-related resource including other AICIE compliant framework listings, technical specifications, repositories, or enrichment information including AI BOMs and knowledge graphs. See [i.32]. The inclusion of AICIE framework compliant lists as a resource enables directory instantiations to be autonomously created, replicated, and synchronized among any accessible network servers - including among a closed communities using any desired medium. Figure 5.2-1: Mindmap of the AICIE Framework Resource Record |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.1 AICIEverison | This required value describes the AICIE Framework Resource Record specification version being used and expressed as three numbers separated by periods and set initially to 1.0.0. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 14 |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.2 AICIEresourcetype | This required value describes the AI incident reporting resource type in the following non-case-sensitive enumeration: Table 5.2.2-1 list Another AICIE Framework Resource list directory based on the present document spec Any technical specification for AI incident information reporting repository Any repository for AI incident information reporting enrichment Any information providing AI incident enrichment information including AI BOMs and knowledge graphs … A one word description of any other resource |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.3 AICIEresourcename | This required value describes the name of the resource without any constraints - prefaced by any related identifier associated with the name. EXAMPLE: "ETSI TS 104 158-1 V1.1.1, Securing Artificial Intelligence (SAI); AI Incident Reporting; Part 1: AI Common Incident Expression (AICIE) Global Framework". |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.4 AICIEresourceaddress | This required value describes the resource address location without any constraints - preferably as a persistent, precise, accessible Uniform Resource Identifier (URI) or a link capable of providing the resource. EXAMPLE: https://www.etsi.org/deliver/etsi_ts/104100_104199/10415801. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.5 AICIEresourcecontact | This required value describes the contact information without any constraints for the entity maintaining and accessing the resource preferably including physical and location and useable email address. EXAMPLE: "ETSI Secretariat, ETSI 650, Route des Lucioles 06560 Valbonne - Sophia Antipolis FRANCE, secretariat@etsi.org". |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.6 AICIEresourceaccess | This optional value without any constraints describes any resource access controls. EXAMPLE: "Members only". The default when leaving blank is publicly available without constraints. |
ab240e5dec472d4fd2871b8208bf1154 | 104 158-1 | 5.2.7 AICIEresourceadd | This optional value without any constraints describes any useful additional attributes of the resource. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 15 Annex A (normative): AICIE Framework Resource Record JSON Format V1.0.1 { "$schema": "tbd", "$id": "tbd2", "properties": { "AICIEversion": { "description": "AICIE Framework Resource Record specification version being used and expressed as three numbers separated by periods and set initially to 1.0.1", "type": "string" }, "AICIEresourcetype": { "description": "value describing AI incident reporting resource type", "type": "string" "enum": [ "list", "spec", "repository", "enrichment" ] "AICIEresourcename": { "description": "name of the resource prefaced by any related identifier associated with the name", "type": "string" }, "AICIEresourceaddress": { "description": "The resource address location without any constraints - preferably as a persistent, precise, accessible Uniform Resource Identifier (URI) or a link capable of providing the resource", "type": "string" }, "AICIEresourcecontact": { "description": "The contact information without any constraints for the entity maintaining and accessing the resource preferably including physical location and useable email address", "type": "string" }, "AICIEresourceaccess": { "description": "describes any resource access controls", "type": "string" }, "AICIEresourceadd": { "description": "any useful additional attributes of the resources", "type": "string" } }, "required": [ "AICIEversion", "AICIEresourcetype", "AICIEresourcename", "AICIEresourceaddress", "AICIEresourcecontact" ] } ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 16 Annex B (informative): ETSI AICIE Framework Resource Record Example AICIEversion 1.0.1 AICIEresourcetype spec AICIEresourcename ETSI TS 104 158-1 V1.0.1, Securing Artificial Intelligence (SAI); AI Incident Reporting; Part 1: AI Common Incident Expression (AICIE) Global Framework AICIEresourceaddress https://www.etsi.org/deliver/etsi_ts/104100_104199/10415801 AICIEresourcecontact ETSI Secretariat, ETSI 650, Route des Lucioles 06560 Valbonne - Sophia Antipolis FRANCE, secretariat@etsi.org AICIEresourceaccess AICIEresourceadd ETSI implementation specification for AICIE resource records ===== AICIEversion 1.0.1 AICIEresourcetype spec AICIEresourcename ETSI TS 104 158-2 V1.0.1, Securing Artificial Intelligence (SAI); AI Incident Reporting; Part 2: AI Common Incident Expression (AICIE) Container AICIEresourceaddress https://www.etsi.org/deliver/etsi_ts/104100_104199/10415802 AICIEresourcecontact ETSI Secretariat, ETSI 650, Route des Lucioles 06560 Valbonne - Sophia Antipolis FRANCE, secretariat@etsi.org AICIEresourceaccess AICIEresourceadd ETSI implementation specification for AICIE resource records ===== AICIEversion 1.0.1 AICIEresourcetype repository AICIEresourcename AICIE Framework Resources Known to ETSI AICIEresourceaddress https://forge.etsi.org/rep/explore/projects [TBD] AICIEresourcecontact ETSI Secretariat, ETSI 650, Route des Lucioles 06560 Valbonne - Sophia Antipolis FRANCE, secretariat@etsi.org AICIEresourceaccess AICIEresourceadd ETSI implementation of AICIE resource record framework ===== AICIEversion 1.0.1 ….. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 17 Annex C (informative): Bibliography • CSET, Ren Bin Lee Dixon and Heather Frase: "An Argument for Hybrid AI Incident Reporting", Center for Security and Emerging Technology, March 2024. • Georgetown-CSET GitHub: "CSET-AIID-harm-taxonomy", June 2024. • Federation of American Scientists (FAS), John Croxton et al.: "Message Incoming: Establish an AI Incident Reporting System", 25 Jun 2024. • MIT, Peter Slattery et al.: "The AI Risk Repository: A Comprehensive Meta-Review, Database, and Taxonomy of Risks From Artificial Intelligence". • MITRE: "AI Assurance - Challenges, Maturity, and Paths Forward", June 2024. • Frontier Model Forum: "FMF Announces First-Of-Its-Kind Information-Sharing Agreement", March 2025. ETSI ETSI TS 104 158-1 V1.1.1 (2026-03) 18 History Version Date Status V1.1.1 March 2026 Publication |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 1 Scope | The present document: 1) Specifies a CBOR [1] format for AdES signatures (CB-AdES signatures hereinafter) built on CBOR Object Signing and Encryption (COSE hereinafter) as specified in IETF RFC 9052 [2]. For this, the present document: - Extends the CBOR Object Signing and Encryption specified in IETF RFC 9052 [2] by defining an additional set of CBOR header parameters that can be incorporated in the COSE Headers (either in its protected headers map or its unprotected headers map). Many of these new header parameters have the same semantics as the attributes/properties defined in CAdES [i.2], XAdES [8], and JAdES [9] digital signatures. Other header parameters are defined to meet specific requirements that current COSE cannot meet (e.g. for explicitly referencing detached payload). These new header parameters and their corresponding types are defined in CDDL, which is specified in IETF RFC 8610 [5]. - Specifies the mechanisms for incorporating the aforementioned CBOR components in COSE [2] to build CB-AdES signatures, offering the same features as CAdES, XAdES, and JAdES in CBOR syntax, and therefore fulfilling the same requirements (such as the long-term validity of digital signatures). 2) Defines four levels of CB-AdES baseline signatures addressing incremental requirements to maintain the validity of the signatures over the long term. Each level requires the presence of certain CB-AdES header parameters, suitably profiled for reducing the optionality as much as possible. The aforementioned levels provide the basic features necessary for a wide range of business and governmental use cases for electronic procedures and communications to be applicable to a wide range of communities when there is a clear need for interoperability of digital signatures used in electronic documents. NOTE 1: ETSI EN 319 102-1 [i.3] specifies procedures for creation, augmentation and validation of other types of AdES digital signatures. Procedures for creation, augmentation, and validation of CB-AdES digital signatures are out of scope. The present multi-part deliverable aims at supporting electronic signatures independent of any specific regulatory framework. NOTE 2: Specifically, but not exclusively, it is the aim that CB-AdES digital signatures specified in the present multi-part deliverable can be used to meet the requirements of electronic signatures, advanced electronic signatures, qualified electronic signatures, electronic seals, advanced electronic seals, and qualified electronic seals as defined in Regulation (EU) No 910/2014 [i.1]. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 2 References | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 2.1 Normative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. Referenced documents which are not found to be publicly available in the expected location might be found in the ETSI docbox. NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee their long-term validity. The following referenced documents are necessary for the application of the present document. [1] IETF RFC 8949 (December 2020): "Concise Binary Object Representtion (CBOR)". [2] IETF RFC 9052 (August 2022): "CBOR Object Signing and Encryption (COSE): Structures and Process". ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 8 [3] IETF RFC 9360 (February 2023): "CBOR Object Signing and Encryption (COSE): Header Parameters for Carrying and Referencing X.509 Certificates". [4] IETF RFC 9053 (August 2022): "CBOR Object Signing and Encryption (COSE): Initial Algorithms". [5] IETF RFC 8610 (June 2019): "Concise Data Definition Language (CDDL): A Notational Convention to Express Concise Bingary Object Representation (CBOR) and JSON Data Structures". [6] IETF RFC 9338 (December 2022): "CBOR Object Signing and Encryption (COSE): Countersignatures". [7] IETF RFC 3061 (February 2001): "A URN Namespace of Object Identifiers". [8] ETSI EN 319 132-1: "Electronic Signatures and Trust Infrastructures (ESI); XAdES digital signatures; Part 1: Building blocks and XAdES baseline signatures". [9] ETSI TS 119 182-1: "Electronic Signatures and Trust Infrastructures (ESI); JAdES digital signatures; Part 1: Building blocks and JAdES baseline signatures". [10] IETF RFC 5035 (August 2007): "Enhanced Security Services (ESS) Update: Adding CertID Algorithm Agility". [11] Recommendation ITU-T X.509: "Information technology - Open Systems Interconnection - The Directory: Public-key and attribute certificate frameworks". [12] IETF RFC 3161 (August 2001): "Internet X.509 Public Key Infrastructure Time Stamp Protocol (TSP)". [13] IETF RFC 5280 (May 2008): "Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile". [14] IETF RFC 6960 (June 2013): "X.509 Internet Public Key Infrastructure Online Certificate Status Protocol - OCSP". [15] IETF RFC 5816 (April 2010): "ESSCertIDv2 Update for RFC 3161". [16] IETF RFC 9597 (June 2024): "CBOR Web Token (CWT) Claims in COSE Headers". [17] IETF RFC 4648 (October 2006): "The Base16, Base32, and Base64 Data Encodings". [18] IETF RFC 8932 (May 2018): "CBOR Web Token (CWT)". [19] ETSI TS 119 312 (V1.3.1): "Electronic Signatures and Infrastructures (ESI); Cryptographic Suites". [20] IETF RFC 3986 (January 2005): "Uniform Resource Identifier (URI): Generic Syntax". [21] IETF RFC 2616 (June 1999): "Hypertext Transfer Protocol - HTTP/1.1". |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 2.2 Informative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee their long-term validity. The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's understanding, but are not required for conformance to the present document. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 9 [i.1] Regulation (EU) 2024/1183 of the European Parliament and of the Council of 11 April 2024 amending Regulation (EU) No 910/2014 as regards establishing the European Digital Identity Framework. [i.2] ETSI EN 319 122-1: "Electronic Signatures and Infrastructures (ESI); CAdES digital signatures; Part 1: Building blocks and CAdES baseline signatures". [i.3] ETSI EN 319 102-1: "Electronic Signatures and Trust Infrastructures (ESI); Procedures for Creation and Validation of AdES Digital Signatures; Part 1: Creation and Validation". [i.4] ETSI TR 119 000: "Electronic Signatures and Infrastructures (ESI); The framework for standardization of digital signatures and trust services; Overview". [i.5] ETSI TR 119 001: "Electronic Signatures and Infrastructures (ESI); The framework for standardization of signatures; Definitions and abbreviations". [i.6] ETSI TR 119 100: "Electronic Signatures and Infrastructures (ESI); Guidance on the use of standards for signature creation and validation". [i.7] ETSI TS 119 172-1: "Electronic Signatures and Infrastructures (ESI); Signature Policies; Part 1: Building blocks and table of contents for human readable signature policy documents". [i.8] OASIS Standard: "Assertions and Protocols for the OASIS Security Assertion Markup Language (SAML) V2.0". [i.9] ETSI TS 101 533-1: "Electronic Signatures and Infrastructures (ESI); Data Preservation Systems Security; Part 1: Requirements for Implementation and Management". [i.10] IETF RFC 4998: "Evidence Record Syntax (ERS)". [i.11] W3C® Recommendation (19 November 2019): "Verifiable Credentials Data Model 1.0". [i.12] draft-cavage-http-signatures-10 (May 2018): "Signing HTTP Messages". [i.13] IETF RFC 7517 (May 2015): "CBOR Web Key (JWK)". [i.14] ISO 3166-1: "Codes for the representation of names of countries and their subdivisions — Part 1: Country code". 3 Definition of terms, symbols, abbreviations and terminology |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 3.1 Terms | For the purposes of the present document, the terms given in ETSI TR 119 001 [i.5], IETF RFC 9052 [2] and the following apply: byte string: sequence of octets (8 bits) CB-AdES signature: COSE signature meeting the requirements specified in this or other parts of the present multi-part document CBOR-bstr-wrapped: CBOR object wrapped in a CBOR byte string (bstr) CBOR byte string: data item of CBOR major type 2 NOTE: The present document uses the term "bstr" for denoting a CBOR byte string. COSE signature: CBOR structure containing a digitally signed message NOTE: As specified in IETF RFC 9052 [2]. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 10 COSE Payload: binary data object (message(s), document(s)) that is signed by a COSE signature NOTE 1: The COSE Payload bytes is only one of the components that contribute to generate the input byte string to the COSE signature value computation process, along with the protected headers sierialized map and the externally supplied data. See IETF RFC 9052 [2], clause 4.3 for externally supplied data, and IETF RFC 9052 [2], clause 4.4 for a specification of the signing process. NOTE 2: The present document allows for a CB-AdES signature to simultaneously sign a set of more than one binary data objects. Therefore the COSE Payload may have more than one components. See clause 5.2.8 (sigD header parameter) of the present document. NOTE 3: The present document allows for COSE Payload to be attached or detached. See clause 5.2.8 (sigD header parameter) of the present document. COSE signature value: digital signature cryptographic value calculated over a sequence of octets derived from the protected headers serialized map, the COSE Payload, and the externally supplied data encapsulated in a CBOR byte string (bstr herein after) NOTE 1: IETF RFC 9052 [2] does not provide any formally defined term for this object; instead it uses the term "signature value". The present document does not use this term, but "COSE signature value", for the sake of terminological coherence of other AdES specifications. NOTE 2: The present document uses the term CBOR Object Signing and Encryption (or its abbreviation, COSE), as defined by IETF RFC 9052 [2], i.e. for denoting the CBOR data structure for representing a digitally signed message. NOTE 3: A CB-AdES signature is a special type of CBOR Object Signing and Encryption signature (or COSE signature). Therefore these terms are not directly interchangeable as, indeed a CB-AdES signature IS ALSO A COSE Signature, but NOT ALL COSE signatures are CB-AdES signatures. electronic time-stamp: data in electronic form which binds other electronic data to a particular time establishing evidence that these data existed at that time NOTE 1: In the case of IETF RFC 3161 [12] protocol, updated by IETF RFC 5816 [15], the electronic time-stamp is referring to the timeStampToken field within the TimeStampResp element (the TSA's response returned to the requesting client). NOTE 2: This definition makes CB-AdES signatures not to be bound to a particular format of electronic time-stamp, because header parameters adoTst, sigTst, arcTst, rfsTst, and sigRTst can contain electronic time-stamps of any format. payload field: payload field of the COSE_Sign CBOR array as specified in clause 4.1 of IETF RFC 9052 [2], or of the payload field of the COSE_Sign1 CBOR array as specified in clause 4.2 of IETF RFC 9052 [2] |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 3.2 Symbols | Void. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 3.3 Abbreviations | For the purposes of the present document, the following abbreviations apply: ASCII American Standard Code For Information Interchange ASN.1 Abstract Syntax Notation 1 bstr byte string CA Certification Authority CBOR Concise Binary Object Representation CDDL Concise Data Definition Language COSE CBOR Object Signing and Encryption CRL Certificate Revocation List FIPS Federal Information Processing Standards HTTP Hyper Text Transfer Protocol ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 11 IETF Internet Engineering Task Force ISO International Organization for Standardization ITU-T International Telecommunication Union - Telecommunication JSON JavaScript Object Notation OCSP Online Certificate Status Protocol OID Object IDentifier PKI Public Key Infrastructure RFC Request For Comments SAML Security Assertion Markup Language SHA Secure Hash Algorithm SIM Subscriber Identification Module SPO Service Provision Option tstr text string URI Uniform Resource Identifier URL Uniform Resource Locator URN Uniform Resource Name UTC Coordinated Universal Time |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 3.4 Terminology | The present document adopts, wherever it is possible the same terminology as the terminology used in IETF RFC 9052 [2]. Therefore, within the present document, the term "COSE Signature" shall denote the CBOR structure for digital signatures specified in IETF RFC 9052 [2]. The present document uses the term "CBOR data item" or just "data item" for denoting any of the data items specified in clause 2 of IETF RFC 8949 [1]. The present document uses the term "header parameter" for denoting a CBOR data item which is member either of the protected headers map or the unprotected headers map of COSE as specified in IETF RFC 9052 [2]. The present document uses the term "member" of a map for denoting one pair of CBOR data items (key, value) within the CBOR map (specified in IETF RFC 8949 [1]. The present document uses the term "element" or "element of the array" for denoting the contents of a position within a CBOR array (specified in of IETF RFC 8949 [1]). NOTE: These last terms will be used for denoting each of the CBOR data items that will be added to the uHeaders CBOR array (specified in clause 5.3.1 of the present document), which will be incorporated in the unprotected headers set as a header parameter. Therefore, these CBOR data items will play, within the present document, an equivalent role to the role played by the unsigned attributes in CAdES and the unsigned qualifying properties in XAdES. The present document uses the term "CB-AdES component" or "component" for denoting any CB-AdES signature constituent, regardless it is a header parameter, a member of a CBOR map, an element of a CBOR array, or any other CBOR data item. IETF RFC 9052 [2] defines two structures for COSE signatures, namely: COSE_Sign (which allows several signatures on the same COSE Payload), and COSE_Sign1 (which only allows one signature on the COSE Payload). Below follows a copy of the CDDL specification for the COSE_Sign structure: COSE_Sign = [ Headers, payload : bstr / nil, signatures : [+COSE_Signature] ] COSE_Signature = [ Headers, signature : bstr ] ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 12 COSE_Sign structures can include header parameters in two layers: 1) Body layer. The header parameters appear within the first element (Headers) of the COSE_Sign CBOR array. 2) Signer layer. This layer is formed by the different elements in the signatures CBOR array, each one being a COSE_Signature array. The signer layer header parameters are included within the first element (Headers) of the COSE_Signature array. Below follows a copy of the CDDL specification for the COSE_Sign1 structure: COSE_Sign1 = [ Headers, payload : bstr / nil, signature :bstr ] COSE_Sign1 structures have only one layer: the body layer. Therefore, COSE_Sign1 structures include all the header parameters only in the body layer. The present document uses this special font for denoting the names of CB-AdES components and COSE structures supporting CB-AdES signatures. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4 General Requirements | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.1 CDDL definitions | The present document defines the new types and components for CB-AdES signatures using the Concise Data Definition Language (CDDL) [5]. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.2 Requirements on CB-AdES supporting COSE structures | CB-AdES signatures specified in the present document may be built on both types of COSE signature structures specified in IETF RFC 9052 [2], namely: COSE_Sign for multiple signers of the same COSE Payload (specified in clause 4.1 of IETF RFC 9052 [2]), and COSE_Sign1 for one single signer (specified in clause 4.2 of IETF RFC 9052 [2]). |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.3 Requirements on CB-AdES encoding | CB-AdES signatures specified in the present document may be encoded as untagged (COSE_Sign and COSE_Sign1) or tagged, namely COSE_Sign_Tagged (specified in clause 4.1 of IETF RFC 9052 [2]), and COSE_Sign1_Tagged (specified in clause 4.2 of IETF RFC 9052 [2]). |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.4 Requirements on CB-AdES headers | The unprotected headers map in CB-AdES signatures (regardless they are within the body layer or the signer layer) shall contain only one member, namely the uHeaders header parameter (specified in clause 5.3 of the present document), which is defined as a CBOR array. NOTE 1: The rationale for this is that the unprotected header is a CBOR map, and no order may be inferred in its different members. This is the reason why the present document defines uHeaders header parameter as a CBOR array. NOTE 2: The elements of this CBOR array will contain CBOR values that play for CB-AdES signatures the same role as the role played by the unsigned header parameters for JAdES signatures. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 13 NOTE 3: An immediate consequence is that a time-stamp token present within the arcTst component specified in clause 5.3.5 of the present document, protects the COSE Payload, the protected headers map, the COSE signature value, and the uHeaders header parameter within the unprotected headers map. CB-AdES signatures supported by a COSE_Sign structure: 1) May include protected header parameters in both, the body layer and the signer layer. 2) Shall not contain the uHeaders unprotected header parameter in the body layer. 3) May include the uHeaders unprotected header parameter in the signer layer. CB-AdES signatures supported by a COSE_Sign1 structure shall include header parameters at the body layer (as they do not have signer layer). New header parameters defined in the present document, or already defined elsewhere but further profiled in the present document, shall be incorporated into the CB-AdES signature as specified in the present document. NOTE 4: For each header parameter newly defined or defined elsewhere but further specified by the present document, the present document specifies if it is signed or/and unsigned, and, in the case of CB-AdES signatures supported by COSE_Sign structures, the layer where it is placed. Header parameters defined elsewhere and not further profiled by the present document, may also be added as signed header parameters or as elements of uHeaders header parameter within the CB-AdES, in accordance with the requirements defined in the present document (for example, those applicable to CB-AdES signatures supported by a COSE_Sign structure). Their semantics and processing are out of the scope of the present document. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.5 Requirements on Payload | In CB-AdES signatures, the COSE Payload may be attached or detached. Detached COSE Payload may either be one detached object, or result from the concatenation of more than one detached data objects. See the specification of sigD signed header parameter in clause 5.2.8 of the present document. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.6 Requirements on map keys and data tags | The present document specifies new CBOR types with the following criteria: • The keys of the CBOR maps pairs shall be integers. • Tags for new CBOR tagged data items shall also be integers. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 4.7 Requirements on encapsulation in CBOR byte strings | The present document requires to encapsulate CBOR components in CBOR byte strings. This encapsulation shall be performed using the CBOR encoding restrictions defined in clause 9 of IETF RFC 9052 [2]. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5 Header parameters semantics and syntax | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1 Header parameters defined by IETF | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.1 Introduction | This clause defines additional requirements for the use of some of header parameters specified by IETF. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 14 NOTE: Clause 6.3 specifies requirements (mainly of presence and cardinality), for the use of some of the header parameters specified by IETF for CB-AdES baseline signatures. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.2 The alg (algorithm) header parameter | Semantics The alg header parameter shall be a signed header parameter that qualifies the signature. The alg header parameter shall have the semantics specified in IETF RFC 9052 [2], clause 3.1. Syntax The alg header parameter shall have the syntax specified in IETF RFC 9052 [2], clause 3.1. Its value should be one of the algorithms for digital signatures recommended by in ETSI TS 119 312 [19]. The identifier of the algorithm shall be one of the identifiers registered at the IANA "COSE Algorithms" (https://www.iana.org/assignments/cose/cose.xhtml#algorithms). In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.3 The content type (content type) header parameter | Semantics The content type header parameter shall be a signed header parameter that qualifies the COSE Payload. The content type header parameter shall have the semantics specified in IETF RFC 9052 [2], clause 3.1. The content type header parameter shall not be present if the sigD header parameter, specified in clause 5.2.8 of the present document, is present within the CB-AdES signature. The content type header parameter should not be present if the content type is implied by the COSE Payload. The content type header parameter shall not be present if the COSE Payload is a (counter-signed) signature. Syntax The content type header parameter shall have the syntax specified in IETF RFC 9052 [2], clause 3.1. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. NOTE: This is because this header parameter qualifies the COSE Payload. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.4 The kid (key identifier) header parameter | Semantics The kid header parameter shall be a signed header parameter that qualifies the signature. The kid header parameter shall have the semantics specified in IETF RFC 9052 [2], clause 3.1. The content of kid header parameter should be the DER-encoded instance of type IssuerSerial type defined in IETF RFC 5035 [10]. The header parameter kid shall be used as a hint that can help to identify the signing certificate if other header parameters referencing or containing the signing certificate are present in the CB-AdES signature. Syntax The kid header parameter shall have the syntax specified in IETF RFC 9052 [2], clause 3.1. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 15 |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.5 The x5u (X.509 URL) header parameter | Semantics The x5u header parameter shall be a signed header parameter that qualifies the signature. The x5u header parameter shall have the semantics specified in IETF RFC 9360 [3], clause 2. The x5u member shall be used as a hint, as implementations can have alternative ways for retrieving the referenced certificate if it is not found at the referenced place. Syntax The x5u header parameter shall have the syntax specified in IETF RFC 9360 [3], clause 2. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.6 The CBOR component for counter signatures | Semantics The CBOR component for including one or more counter signatures shall be an element of the uHeaders member of the unprotected headers map (see clause 5.3.1 of the present member for the specification of uHeaders member). The element of the uHeaders member of the unprotected headers map used for including one or more counter signatures shall be either one of the two header parameters specified in IETF RFC 9338 [6], or a CB-AdES signature as specified in the present document. Syntax Each CBOR component for incorporating new counter signatures shall contain either one of the two header parameters specified in IETF RFC 9338 [6]. NOTE 1: The use of a counter signature as specified in IETF RFC 9338 [6] or a CB-AdES as a counter signature is use-case or policy dependent. Therefore, the present document does not make any recommendation in this sense. Discriminating whether the counter signature is a CB-AdES can be done by checking its sets of protected and unprotected header parameters; if the contents of these sets are according to the requirements in Table 14 in clause 6.3 of the present document, then the counter signature is a CB-AdES signature. Otherwise, it is a non CB-AdES counter signature. The digital signature value of each counter signature shall be computed as specified in clause 3.3 of IETF RFC 9338 [6]. If the counter signature is a CB-AdES signature, its digital signature value shall be computed as specified in clause 3.3 of IETF RFC 9338 [6] In these cases the context string shall be selected according to the structure used by the CB-AdES counter signature. NOTE 2: Clause 4.4 states that CB-AdES signatures supported by a COSE_Sign structure, only have unprotected header in the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.7 The x5t (cert hash) header parameter | Semantics The x5t header parameter shall have the semantics specified in IETF RFC 9360 [3], clause 2. Syntax The x5t header parameter shall have the syntax of COSE_CertHash type, specified in IETF RFC 9360 [3], clause 2. COSE_CertHash = [ hashAlg: (int / tstr), hashValue: bstr ] ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 16 NOTE: The x5t component is an array of two elements. The first one identifies a digest algorithm. The second one contains the digest value of a certificate DER-encoded. See in IETF RFC 9360 [3], clause 2 for further details on the types of each component of this array. The value of the hash identifier (first element in the CBOR array -hashAlg) shall be one of the identifiers for digest algorithms registered in IANA COSE Algorithms registry (https://www.iana.org/assignments/cose/cose.xhtml#algorithms), or any future specification that defines new identifiers for digest algorithms. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.8 The x5chain (X.509 chain) header parameter | Semantics The x5chain header parameter may be a signed or unsigned header parameter that qualifies the signature. If x5chain has not to be signed, it shall be included within the uHeaders CBOR array specified in clause 5.3.1 of the present document. NOTE: This is for meeting requirements appearing in use cases that require to sign the digest of the signing certificate but not the signing certificate itself, while also requiring to include it within the CB-AdES signature. The x5chain header parameter shall have the semantics specified in IETF RFC 9360 [3], clause 2. Syntax The x5chain header parameter shall have the syntax of COSE_X509 type specified in IETF RFC 9360 [3], clause 2. COSE_X509 = bstr / [ 2*certs: bstr ] If the x5chain is unsigned, it shall be placed within the uHeaders CBOR array (and therefore within the unprotected header). In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed in the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.9 The iat (issued at) header parameter | Semantics The iat header parameter may be a signed or unsigned header parameter that qualifies the signature. The iat header parameter shall have the semantics specified in clause 3.1.6 of IETF RFC 8392 [18]. The iat header parameter's value shall specify the time at which the signer claims to have performed the signing process. Syntax The syntax of the iat header parameter shall be as specified in IETF RFC 8932 [18], clause 3.1.6. Its value shall be an instance of NumericDate, which is specified in IETF RFC 8932 [18], clause 2. The iat header parameter shall be incorporated to CB-AdES signature within the CWT claim specified in IETF RFC 9597 [16]. This CWT claim shall be incorporated to the CB-AdES signature as a signed header parameter as specified in IETF RFC 9597 [16]. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.1.10 The crit (critical) header parameter | Semantics ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 17 The crit header parameter shall be a signed header parameter that qualifies the signature. The crit header parameter shall have the semantics specified in IETF RFC 9052 [2], clause 3.1. Syntax The crit header parameter shall have the syntax specified in IETF RFC 9052 [2], clause 3.1. If the CB-AdES signature includes the sigD header parameter, the crit header parameter shall also be present and the label assigned to the sigD header parameter shall be one of its CBOR array elements. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2 New signed header parameters | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.1 Labels and tags of the signed header parameters | Clause 5.2 specifies a number of signed header parameters. All of them shall be part of the protected headers map when present. All of them shall be identified by a label in the corresponding CBOR map that shall be an integer. Table 1 below defines the labels associated to each signed header parameter and the tags identifying each signature policy qualifier specified in clause 5.2. Table 1: Tags for signed header parameters specified in the present document Signed header parameter type name Signed header parameter label and qualifiers tags x5ts 261 srCms 262 sigPl 263 srAts 264 adoTst 265 sigPId 266 sigD 267 The following CDDL rules assign the integer values of the tags of the signed header parameters to identifiers that will be used throughout the rest of the present document for making the CDDL rules easier to read. NOTE: In the CDDL rules that appear below the _l stands for "label". x5ts_l = 261 srCms_l = 262 sigPl_l = 263 srAts_l = 264 adoTst_l = 265 sigPId_l = 266 sigD_l = 267 ETSI_Signed_Headers = ( ? 33 => x5chain, ;X.509 cert path or signing certificate ? x5ts_l => x5ts, ;Reference to signing certificate / certs in cert path ? srCms_l => srCms, ;Signer commitments ? sigPl_l => sigPl, ;Signature production place ? srAts_l => srAts, ;Signer attributes ? adoTst_l => adoTst, ;COSE payload time-stamp ? sigPId_l => sigPId, ;Signature Policy Identifier ? sigD_l => sigD ;Detached COSE Payload reference data ) |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.2 The x5ts (X.509 certificates Thumbprints) header parameter | Semantics The x5ts header parameter shall be a signed header parameter that qualifies the signature. Its label shall be 261. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 18 The x5ts header parameter shall contain several references of certificates within the certification path of the signing certificate, each one formed by the identifier of a digest algorithm and the digest value of the referenced certificate. The first reference within the x5ts header parameter shall be the reference of the signing certificate. The x5ts header parameter shall not contain any other information. Syntax Below follows the CDDL definition of the x5ts header parameter: x5ts = [2*x5t: COSE_CertHash] Each x5t component of x5ts shall have the semantics and syntax specified in IETF RFC 9360 [3], clause 2 and further profiled in clause 5.1.7 of the present document. A CB-AdES signature shall have at least one of the following header parameters in the protected headers map: x5t, x5ts, or x5chain (specified in IETF RFC 9360 [3], clause 2 and further profiled in clause 5.1.8 of the present document) for protecting the signing certificate with the signature. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.3 The srCms (signer commitments) header parameter | Semantics The srCms header parameter shall be a signed header parameter that qualifies the COSE Payload. Its label shall be 262. The srCms header parameter shall indicate the commitment made by the signer when signing. The srCms header parameter shall express the commitment type with a URI. The srCms header parameter may contain a sequence of qualifiers providing more information about the commitment. A commitment type associated to a signature is, as specified in clause A.3.2.2 of ETSI TS 119 172-1 [i.7], "the representation of the expected purpose and meaning of the signature and of the precise nature of the responsibility assumed by the signer when generating the concerned signature". NOTE 1: The commitment type can be: defined as part of the signature policy, in which case, the commitment type has precise semantics that are defined as part of the signature policy; or be a registered type, in which case, the commitment type has precise semantics defined by registration, under the rules of the registration authority. Such a registration authority can be a trading association or a legislative authority. NOTE 2: Annex B of ETSI TS 119 172-1 [i.7] has defined a set of commitment types and their corresponding URIs. These URIs are listed in Annex C of the present document. Syntax Below follows the CDDL definition of the srCms header parameter: srCms = [+SrCm] SrCm = { 1 => oId, ;commId the commitment identifier: an oId data type ? 2 => [+any] ;commQuals: qualifiers } Each element of the srCms CBOR array shall indicate one commitment made by the signer, which may be further qualified. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 19 The commId member of the SrCm CBOR map is an instance of oId type (a CBOR map), which is specified in clause 5.4.1 of the present document. The id member of oId shall have a URI as value, uniquely identifying one commitment made by the signer. The commQuals member of the SrCm CBOR map provides means to include additional qualifying information on the commitment made by the signer. NOTE 3: None of the commitment types defined in Annex B of ETSI TS 119 172-1 [i.7] have commitment qualifiers. The specification of additional qualifying information of the commitment is out of the scope of the present document. Any specification defining a new commitment type that requires additional qualifying information, shall provide a full definition of the semantics and the syntax of the mentioned qualifying information. Table 2 shows the values assigned to each of the keys in the maps specified in the present clause. Table 2: Values of keys in maps specified in the present clause Name Key value in map commId 1 commQuals 2 In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.4 The sigPl (signature production place) header parameter | Semantics The sigPl header parameter shall be a signed header parameter that qualifies the signer. Its label shall be 263. The sigPl header parameter shall specify an address associated with the signer at a particular geographical (e.g. city) location. Syntax Below follows the CDDL definition of the sigPl header parameter: NOTE: Its definition follows the specification of PostaAddress type in schema.org (https://schema.org/PostalAddress). sigPl = { ? 1 => tstr, ; addressCountry ? 2 => tstr, ; addressLocality ? 3 => tstr, ; addressRegion ? 4 => tstr, ; postOfficeBoxNumber ? 5 => tstr, ; postalCode ? 6 => tstr, ; streetAddress } The sigPl header parameter shall have at least one of its members. The addressCountry member shall contain may contain either the name of the country or its two-letter ISO 3166-1 [i.14] alpha-2 country code. Table 3 shows the values assigned to each of the keys in the maps specified in the present clause. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 20 Table 3: Values of keys in maps specified in the present clause Name Label in map addressCountry 1 addressLocality 2 addressRegion 3 postOfficeBoxNumber 4 postalCode 5 streetAddress 6 In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.5 The srAts (signer attributes) header parameter | Semantics The srAts header parameter shall be a signed header parameter that qualifies the signer. Its label shall be 264. The srAts header parameter shall encapsulate signer attributes (e.g. role). This header parameter may encapsulate the following types of attributes: • attributes claimed by the signer; • attributes certified in attribute certificates issued by an Attribute Authority; or/and • assertions signed by a third party. Syntax Below follows the CDDL definition of the srAts header parameter: srAts = { ? 1 => CertifiedAttrs, ;certified: Certified signer attributes ? 2 => AttrArrays, ; signedAssertions: Signed assertions for signer ? 3 => AttrArrays ; claimed: Claimed signer attributes } CertifiedAttrs = [ + CertifiedAttr] CertifiedAttr = {CertifiedAttrChoice} CertifiedAttrChoice = ( 1 => pkiObj // ;x509AttrCert: encapsulates a X.509 attribute certificate 2 => pkiObj ; otherAttrCert: encapsulates another type of attribute certificate ) AttrArrays = [+NotCertifiedItem] label = int / tstr value = any NotCertifiedItem = [ mediaType : tstr, ;String identifying the media type of claimed attributes or signed ;assertions *label => any the not certified item ] The certified member shall contain a non-empty array of certified attributes, which shall be one of the following: • the DER-encoded X.509 attribute certificates conformant to Recommendation ITU-T X.509 [11] issued to the signer, within the x509AttrCert member; or • attribute certificates (issued, in consequence, by Attribute Authorities) in different syntax than the one specified in Recommendation ITU-T X.509 [11], within the otherAttrCert member. The definition of specific otherAttrCert is outside of the scope of the present document. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 21 The signedAssertions member shall contain a non-empty array of assertions signed by a third party. NOTE 1: A signed assertion is stronger than a claimed attribute, since a third party asserts with a signature that the attribute of the signer is valid. However, it is less restrictive than an attribute certificate. EXAMPLE: Verifiable credentials as specified in W3C® Recommendation [i.11] if serialized in CBOR. The claimed member shall contain a non-empty array of attributes claimed by the signer but neither certified by an Attribute Authority, nor signed by any entity issuing assertions. Both the signedAssertions and the claimed members shall be instances of AttrArrays type. Each instance of this type shall be a CBOR array whose elements are instances of NotCertifiedItem CBOR array. Each instance of NotCertifiedItem shall contain two elements, whose contents are specified below: a) The mediaType shall contain a string identifying the media type of the signed assertions or the claimed attributes present in qVals member. NOTE 2: The media types registered by IANA can be found at https://www.iana.org/assignments/media- types/media-types.xhtml#app4lication. Nowadays, the media type registered also include information on the character-set encoding. b) The qVals member, which shall be a CBOR array of at least one item. The elements of qVals CBOR array shall be the values of the signed assertions or the claimed attributes encoded as indicated within the encoding member. NOTE 3: Instances of AttrArrays type allow incorporating signed assertions and/or claimed attributes of different types. The definition of specific content types for signedAssertions and claimed members is outside of the scope of the present document. Empty srAts header parameters shall not be generated. Table 4 shows the values assigned to each of the keys in the maps or groups specified in the present clause. Table 4: Values of keys in maps or groups specified in the present clause Name Label in map certified 1 signedAssertions 2 claimed 3 x509AttrCert (in CertifiedAttrChoice) 1 otherAttrCert (in CertifiedAttrChoice) 2 In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.6 The adoTst (COSE payload time-stamp) header parameter | Semantics The adoTst header parameter shall be a signed header parameter that qualifies the COSE Payload. Its label shall be 265. The adoTst header parameter shall encapsulate one or more electronic time-stamps, generated before the signature production, whose message imprint computation input shall be the COSE Payload of the CB-AdES signature. Syntax Below follows the CDDL definition of the adoTst header parameter: adoTst = tstContainer ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 22 The message imprint computation input for the time-stamp token shall be an octet stream built as indicated below: 1) If the sigD header parameter, as specified in clause 5.2.8 of the present document, is absent then the message imprint computation input shall be: - The CBOR byte string of the payload field, if the payload field is present. - The bytes of the detached COSE Payload, encapsulated in a CBOR byte string, if the COSE Payload is detached (the payload field is absent). 2) Else, if the sigD header parameter is present, if the value of its mId member is "http://uri.etsi.org/19152/ObjectIdByURI" or "http://uri.etsi.org/19152/ObjectIdByURIHash" then concatenate the bytes resulting from processing the contents of its pars member as specified in clause 5.2.8.2.2 of the present document. NOTE: The rationale for applying the processing specified in clause 5.2.8.2.2 of the present document to the case of the mechanism identified by "http://uri.etsi.org/19152/ObjectIdByURIHash" is the fact that this is an indirect signing mechanism, i.e. based on signing digest values of data objects, instead of the data objects themselves. Time-stamping not the digest values but the retrieved data objects, protects against future weaknesses of the digest algorithms used in sigD. 3) Else, if the value of its mId member is neither "http://uri.etsi.org/19152/ObjectIdByURI" nor "http://uri.etsi.org/19152/ObjectIdByURIHash", then it is out of the scope of the present document to specify how to retrieve the COSE Payload, and the specification defining the value of the mId member shall have to specify how to retrieve the COSE Payload. If the COSE Payload is detached and the CB-AdES signature does not incorporate the sigD signed header parameter, then it is out of the scope to specify how to retrieve the COSE Payload. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.7 The sigPId (signature policy identifier) header parameter | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.7.1 Semantics and syntax | Semantics The sigPId header parameter shall be a signed header parameter qualifying the signature. Its label shall be 266. The sigPId header parameter shall contain an explicit identifier of a signature policy. NOTE: ETSI TS 119 172-1 [i.7] specifies a framework for signature policies. Syntax Below follows the CDDL definition of the sigPId header parameter: sigPId = { 1 => oId, ;id: instance of oId type identifying the signature policy 2 => DigAlgVal, ;digAlgVal: digest algorithm and value of the signature policy ;document ? 3 => bool .default false, ;digPSp: indicates whether the digest has been computed according to ;some spec, default value: false ? 4 => [+SigPQual] ;sigPQuals: signature policy qualifiers } DigAlgVal = [ hashAlg: (int / tstr), hashValue: bstr ] The id member shall be used for referencing the signature policy explicitly. It shall uniquely identify a specific version of the signature policy. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 23 The digAlgVal component shall be a CBOR array. Its first element shall be one of the identifiers for digest algorithms registered in IANA COSE Algorithms registry (https://www.iana.org/assignments/cose/cose.xml#algorithms), https://www.iana.org/assignments/cose/cose.xml#header-algorithm-parameters or one of the identifiers for digest algorithms defined in IETF RFC 9053 [4], or any future specification that amends, complements, or supersedes it. Its second element shall be the value of the digest computed on the signature policy document using the algorithm identified in the first element of the CBOR array. The digPSp member shall be a CBOR Boolean value. When present and set to "true", it shall indicate that the digest of the signature policy document has been computed as specified in a technical specification. Absence of this member shall be considered as if present and set to "false". If this member is present and set to "true", then the spDSpec qualifier shall be present and shall identify the aforementioned technical specification. The sigPQuals member shall be a non-empty array of qualifiers of the signature policy. Clause 5.2.7.2 specifies three signature policy qualifiers. Table 5 shows the values assigned to each of the keys in the maps specified in the present clause. Table 5: Values of keys in maps or groups specified in the present clause Name Label in map id 1 digAlgVal 2 digPSP 3 sigPQuals (in CertifiedAttrChoice) 4 In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.7.2 Signature policy qualifiers | Semantics This clause specifies three qualifiers for the signature policy. Each qualifier shall be a CBOR tagged data item. The present document defines the following qualifiers and tags: • A URL where a copy of the signature policy document can be obtained (spURI choice). Its tag shall be 1. • A user notice that should be displayed when the signature is validated (spUserNotice choice). Its tag shall be 2. • An identifier of the technical specification that defines the syntax used for producing the signature policy document (spDSpec choice). Its tag shall be 3. Syntax Below follows the CDDL definitions of the spURI, spUserNotice, and spDSpec qualifiers: SigPQual = { ? 1 => #6.32(tstr) // ;spURI: URL where a copy of the signature policy document ;can be obtained ? 2 => SpUserNotice// ;spUserNotice: Info displayed when signature is validated ? 3 => SpDesc // ;spDSpec: identifier of the technical specification that defines ;the syntax used for producing the signature policy document *label => value ;otherQuals: extension point for qualifiers not specified in ;the present document. Reminder: label is defined in clause 5.2.5 ; either as an uint or a tstr, and value is defined as any. } SpUserNotice = { ? 1 => NoticeRef, ; noticeRef: User notice and references ? 2 => tstr ;explText: notice text to be displayed } NoticeRef = { 1 => tstr, ;org: the name of the organization 2 => [+uint] ;noticeNumbers: the notice numbers identifying textual statements ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 24 } SpDSpec = obId The spURI qualifier shall contain a URL value where a copy of the signature policy document can be obtained. NOTE 1: This URL can reference, for instance, a remote site (which can be managed by an entity entitled for this purpose) from where (signing/validating) applications can retrieve the signature policy document. The spUserNotice qualifier shall contain information that is intended for being displayed whenever the signature is validated. The org member shall indicate the name of the organization. The noticeNumbers shall be a CBOR array with unsigned integers. At least one of the two members of spUserNotice qualifier shall be present. The explText member shall contain the text of the notice to be displayed. The noticeRef member shall name an organization and shall identify by numbers (noticeNumbers member) a group of textual statements prepared by that organization, so that the application could get the explicit notices from a notices file. NOTE 2: Other notices can come from the organization issuing the signature policy. The spDSpec member shall identify the technical specification that defines the syntax used for producing the signature policy document. The otherQuals member shall be a non-empty CBOR array. Each element in the array shall be a qualifier. NOTE 3: The otherQuals member is an extension point for adding qualifiers not specified in the present document. Table 6 shows the values assigned to each of the keys in the maps specified in the present clause. Table 6: Values of keys in maps or groups specified in the present clause Name Label in map spUri 1 spUserNotice 2 spDSpec 3 otherQuals 4 noticeRef (in SPUserNotice) 1 explText (in SPUserNotice) 2 org (in NoticeRef) 1 noticeNumbers (in NoticeRef) 2 |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.8 The sigD header parameter | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.8.1 Semantics and Syntax | Semantics The sigD header parameter shall be a signed header parameter. Its label shall be 267. The sigD header parameter shall not appear in CB-AdES signatures whose COSE Payload is attached. The sigD header parameter may appear in CB-AdES signatures whose COSE Payload is detached. A CB-AdES signature shall have at most one sigD header parameter within each present protected header . ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 25 NOTE 1: When using COSE_Sign, it is possible to build a COSE signature which allows multiple signers to sign the same COSE Payload. Each element of its signatures element includes its own protected header; therefore, each signer can include its own sigD header parameter. The sigD header parameter shall: 1) Reference one or more detached data objects. 2) Specify how the aforementioned references shall be processed for contributing to build the sequence of octets that shall be the COSE Payload of the CB-AdES signature. 3) Allow defining different mechanisms for meeting the two aforementioned requirements. 4) Not be present as a header parameter of a counter signature, as it is clear what an embedded counter signature signs. Chaining of references shall not be allowed. Only the data objects directly referenced within the sigD header parameter shall contribute to build the COSE Payload. If some referenced object contains in its turn references to other data objects, these last data objects shall not contribute to build the COSE Payload. NOTE 2: This is for avoiding building trees of referenced and distributed data objects, which would complicate the validation of CB-AdES signatures. The sigD header parameter may also incorporate digest values of the referenced data objects encapsulated within a CBOR byte string. The sigD header parameter may also incorporate any additional information for meeting requirements 1) and 2) as required by the mechanisms mentioned in 3). Syntax Below follows the CDDL definition of the sigD header parameter: sigD : { 1 => #6.32(tstr), ;mId: URI identifying the mechanism used for referencing and processing each ;referenced data object 2 => [+tstr], ;pars: References to data objects as per the mechanism identified by mId ? 3 => (int / tstr) ;hashM: Digest algorithm identifier ? 4 => [+bstr], ;hashV: Digest values of referenced data objects as per algorithm identified by ;hashM ? 5 => [+tstr] ;ctys: Indication of the content type of each referenced object } The mId member shall be present. It shall be an URI identifying the mechanism used for referencing and processing each referenced data object for building the COSE Payload. The present document defines 2 referencing mechanisms with their corresponding identifiers in clauses 5.2.8.2.2 and 5.2.8.2.3. The pars member shall be present. It shall be a non-empty array of strings. Each element of the array shall contain a reference to one data object, as required by the identification mechanism identified in the mId member. The hashM member shall be the identifier of a digest algorithm. Its value shall be one of the identifiers for digest algorithms registered in IANA COSE Algorithms registry (https://www.iana.org/assignments/cose/cose.xml#algorithms), https://www.iana.org/assignments/cose/cose.xml#header-algorithm-parameters or one of the identifiers for digest algorithms defined in IETF RFC 9053 [4], or any future specification that amends, complements, or supersedes it. The presence of the hashM member shall be conditional on the definition of the identification mechanism. If this member is present, then hashV member shall be present. The hashV member shall be a non-empty array of strings. Each element of the array shall contain the digest value of the data object referenced by the parameter value that is present in the same position of the pars array, encapsulated within a CBOR byte string. The presence of the hashV member shall be conditional on the definition of the identification mechanism. If this member is present, then hashM member shall be present. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 26 The ctys member shall be a non-empty an array of strings. The contents of each element of this array shall have the same semantics of the content type header parameter specified in IETF RFC 9052 [2], clause 3.1. There shall be as many elements within the ctys array as elements within the array pars. Each element of the ctys array shall contain the information corresponding to the data object referenced by the parameter value that is present in the same position of the pars array, except if the content type is implied by the data object or the data object is a counter-signed signature: in these cases, the element of the ctys array shall have as value a null (#7.22 or 0xF6). Table 7 shows the values assigned to each of the keys in the maps specified in the present clause. Table 7: Values of keys in maps or groups specified in the present clause Name Label in map mId 1 pars 2 hashM 3 hashV 4 ctys 5 In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.8.2 Mechanisms supported by URI-references | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.8.2.1 General requirements | This clause specifies two mechanisms that use URI-references for referencing the data objects contributing to build the COSE Payload. For these referencing mechanisms, the contents of the pars member shall be an array of strings. Each string shall be an URI-reference, which, once resolved, shall result in an URI appertaining to the group of URIs that can be classified as locators according to clause 1.1.3 of IETF RFC 3986 [20]. Each URI-reference shall refer one data object. NOTE: According to IETF RFC 3986 [20], URI references that can be classified as locators (URLs are the obvious example) "provide a means of locating the resource by describing its primary access mechanism". When resolving an URI-reference which is a relative reference, conforming application shall set a default base HTTP scheme URI when applying clause 5.1.4 of IETF RFC 3986 [20]. Dereferencing URI-references in the HTTP scheme shall be supported. Dereferencing an URI-reference in the HTTP scheme shall comply with the Status Code Definitions specified in clause 10 of IETF RFC 2616 [21]. Dereferencing URI-references in other locator schemes may be supported. Dereferencing URI-references within one of such schemes shall be conducted as defined in the corresponding scheme specification. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.8.2.2 Mechanism ObjectIdByURI | The URL identifying this referencing mechanism shall be "http://uri.etsi.org/19152/ObjectIdByURI". For this referencing mechanism, neither hashV, nor hashM shall be present. Member ctys may be present. The semantics and syntax of each element of array ctys shall be as specified in clause 5.2.8.1 of the present document. The stream of octets corresponding to the contribution of the COSE Payload to the computation of the COSE signature value shall be generated as indicated below: 1) Initialize the stream of octets to an empty stream. 2) While there are URI-references in the pars array not visited: - Take the next one. - Dereference the URI referemce, as specified in clause 5.2.8.2.1 of the present document. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 27 - Concatenate the resulting octets to the stream of octets that will form the COSE Payload. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.2.8.2.3 Mechanism ObjectIdByURIHash | The URL identifying this referencing mechanism shall be "http://uri.etsi.org/19152/ObjectIdByURIHash". For this referencing mechanism, the hashV, and the hashM members shall be present. Member ctys may be present. The semantics and syntax of hashM, hashV, and ctys shall be as specified in clause 5.2.8.1 of the present document. For computing the digest values that appear within the hashV member, each data object referenced within the pars member, shall be retrieved as specified in clause 5.2.8.2.1 of the present document. When using this mechanism, the COSE Payload shall contribute as an empty stream to the computation of the COSE signature value. NOTE 1: As this sigD is a signed header parameter, and it already includes the digest of the components of the COSE Payload, the COSE Payload is indirectly signed by signing the sigD signed header parameter, and consequently, this referencing mechanism does not require that the COSE Payload directly contributes to the computation of the COSE signature value. If the COSE Payload is required for other purposes than computing the COSE signature value when this mechanism is used, it shall be generated as specified in clause 5.2.8.2.2. NOTE 2: The generation of this COSE Payload is required, for instance, for generating the adoTst or the arcTst header parameters. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3 New unsigned header parameter | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.1 The uHeaders header parameter | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.1.1 Semantics and syntax | Semantics The uHeaders parameter, member of the unprotected headers map, shall be a CBOR array whose elements contain CBOR values that are not signed by the CB-AdES signature. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 28 NOTE 1: The rationale for this is as follows: to allow validating CB-AdES signatures long after they have been created (even after the expiration of the signing certificate, the revocation of some certificate, or even the break of some cryptographic algorithm), all the required material for validating them, and time-stamp tokens that time-stamp all the components of the signatures themselves, are added as unprotected header parameters (they are added after that the digital signature has been created). These time-stamps are called archive time-stamps. As time goes by, it is necessary to add new archive time-stamps for countering new expirations of certificates or new breaks of cryptographic algorithms, for instance. Each newly incorporated archive time-stamp time-stamps all the components in the CB-AdES signature, including the components of the unprotected headers map: the input to its message imprint computation is the result of concatenating all these components. The validation of these augmented signatures requires the verification of the successive archive time-stamps. These verifications require that the message imprint of each archive time-stamp is known without any ambiguity. As CBOR maps do not preserve the order, placing the validation material and the successive archive time-stamps as members of the unprotected headers map would prevent the proper computation of their message imprints. As CBOR arrays define an order, the present document defines the uHeaders parameter as a CBOR array to be the container of all the unsigned header parameters that are added to CB-AdES signature after its generation for achieving long term digital signatures. These unsigned header parameters are placed in the CBOR array in their order of incorporation. In this way, for each new archive time-stamp to be incorporated to the CB-AdES signature, the input to its message imprint computation is built by concatenating, among other things, all the elements present in the uHeaders parameter in the order of appearance within this CBOR array. ETSI EN 319 102-1 [i.3] defines an algorithm for validating digital signatures augmented with archive time-stamps. NOTE 2: As it has been specified in clause 4 of the present document uHeaders header parameter is incorporated in the unprotected headers map specified in clause 3 of IETF RFC 9052 [2]. Consequently, all its elements will also be unprotected. The uHeaders header parameter plays in CB-AdES signatures the same role as the etsiU header parameter in JAdES signatures (which is specified in clause 5.3.1 of ETSI TS 119 182-1 [9]. The uHeaders header parameter shall contain CBOR values that qualify the CB-AdES signature itself, or the signer, or the COSE Payload. New unsigned attributes shall always be added at the end of the uHeaders header parameter, which is a CBOR array. NOTE 3: This implies that the order in the uHeaders header parameter reflects the order in the generation of the CBOR elements. The unsigned attributes shall be encapsulated in CBOR byte strings before being placed within the uHeaders header parameter (CBOR-bstr-wrapped). The present document specifies: 1) A CBOR map (sigPSt) containing details for facilitating access to a signature policy document, in clause 5.3.2. 2) CBOR objects containing details for a counter signature of the CB-AdES signature itself, in clause 5.1.6. 3) A CBOR map (sigTst) containing a time-stamp token on the COSE signature value, in clause 5.3.3. 4) A CBOR map (valData) containing certificates and validation data required for validating the signature, in clause 5.3.4. 5) A CBOR map (arcTst) containing one or more time-stamp tokens on all the components of the CB-AdES signature, in clause 5.3.5. 6) A CBOR map (refs) containing references to certificates and validation data required for validating the signature, in clause A.1.1. 7) A CBOR map (sigRTst) containing a time-stamp token on the references to the validation material and the COSE Signature Value, in clause A.1.2.1. 8) A CBOR map (rfsTst) containing a time-stamp token on the references to the validation material, in clause A.1.2.2. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 29 All the CBOR objects listed above shall be placed within the uHeaders header parameter if they are incorporated into the CB-AdES signature. The uHeaders CBOR array shall be assigned an identifying tag. Additionally, each unsigned attribute shall be assigned a label. Table 8 below defines the labels associated to each unsigned attribute and the tag identifying the uHeaders CBOR array specified in clause 5.3. Table 8: Tags for signed header parameters specified in the present document unsigned attributes type name uHeaders CBOR array tag and unsigned attributes labels uHeaders 268 sigTst 1 valData 2 arcTst 3 refs 4 sigRTst 5 rfsTst 6 sigPSt 7 The following CDDL rules assign the integer values of the labels of the unsigned header parameters to identifiers that will be used throughout the rest of the present document for making the CDDL rules easier to read: uHeaders_l = 268 sigTst_l = 1 valData_l = 2 arcTst_l = 3 refs_l = 4 sigRTst_l = 5 rfsTst_l = 6 sigPSt_l = 7 NOTE 4: While integer labels have been assigned to all the header parameters in the present document, it is allowed to incorporate header parameters defined elsewhere with labels whose values are text strings. Syntax Below follows the CDDL definition of the uHeaders header parameter: uHeaders = [+bstr .cbor UHeaderInstance] ;an array of CBOR byte strings each on encapsulating ;one instance of UHeaderInstance UHeaderInstance = { sigTst_l => sigTst // ;Signature time-stamp OR valData_l => valData // ;Validation data (certificate values and revocation data) OR arcTst_l => arcTst // ;Archive-time-stamp OR refs_l => refs // ;References to certificates and revocation data OR sigRTst_l => sigRTst // ;Signature and references time-stamp OR rfsTst_l => rfsTst // ;References only time-stamp OR sigPSt_l => sigPSt // ; Signature Policy Store OR 11 => COSE_CounterSignature / ;full counter signature as specified in IETF RFC 9338 [+COSE_CounterSignature] // 12 => COSE_CounterSignature0 // ;abbreviated counter signature as specified in IETF RFC 9338 33 => bstr / [2*certs:bstr] // ;x5chain for the signing certificate *label => value ;other additional unsigned attributes not specified in the ;present document } The uHeaders header parameter shall be a non-empty array. In CB-AdES signatures supported by a COSE_Sign structure, this header parameter shall be placed at the signer layer. The uHeaders header parameter shall be incorporated as member of the unprotected header map of the CB-AdES signature. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 30 NOTE 5: The second element of the CBOR array forming the COSE object structure, is the place reserved by IETF RFC 9052 [2] for unsigned header parameters in COSE Signatures. Clause 3.2 of IETF RFC 9052 [2] leaves its content open. The present document suitably profiles its contents. The uHeaders header parameter should be the only header parameter incorporated to the unprotected headers map. Any CBOR value that is not specified in the present document should be incorporated as an element of the uHeaders header parameter. NOTE 6: Adding these components into the uHeaders header parameter allows to properly secure them in the long-term using arcTst. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.2 The sigPSt CBOR map | Semantics The sigPSt CBOR map shall contain either: • the signature policy document which is referenced in the sigPId CBOR map so that the signature policy document can be used for offline and long-term validation; or • a URI referencing a local store where the signature policy document can be retrieved. Syntax Below follows the CDDL definition of the sigPSt header parameter: sigPSt = { 1 => DocOrLocalURI, ;docOrLocalUri: either the signature policy document itself or a URI ;referene to it ?2 => oId ;spDSpec: identifier of the technical specification defining the syntax ;of the signature policy } DocOrLocalURI = { 1 => bstr // ;sigPolDoc: The signature policy document itself 2 => #6.32(tstr), ;sigPolLocalURI: a local URI to the signature policy document } The sigPolDoc member shall contain the signature policy document encapsulated within a CBOR byte string. The sigPolLocalURI member shall have as value the URI pointing to a local store where the present document can be retrieved. NOTE 1: Contrary to the spURI, the sigPolLocalURI points to a local file. The spDSpec member shall identify the technical specification that defines the syntax used for producing the signature policy document. NOTE 2: It is the responsibility of the entity incorporating the signature policy to the signature-policy-store to make sure that the correct document is securely stored. NOTE 3: Being unsigned, the sigPSt is not protected by the digital signature. If the sigPId signed attribute is incorporated into the signature and contains the digAlgVal member with the digest value of the signature policy document, any alteration of the signature policy document present within sigPSt or within a local store, would be detected by the failure of the digests comparison. Table 9 shows the values assigned to each of the keys in the maps specified in the present clause. Table 9: Values of keys in maps or groups specified in the present clause Name Label in map docOrLocalUri (in sigPst) 1 spDSpec (in sigPst) 2 sigPolDoc (in DocOrLocalURI group) 1 sigPolLocalURI(in DocOrLocalURI group) 2 ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 31 |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.3 The sigTst CBOR map | Semantics The sigTst CBOR map shall encapsulate one or more electronic time-stamps time-stamping the COSE signature value. Syntax Below follows the CDDL definition of the sigTst header parameter: sigTst = tstContainer The input of the message imprint computation for the time-stamp tokens encapsulated by sigTst CBOR map shall be the COSE signature value present within the CB-AdES signature. NOTE: This is the same as the content encapsulated within the signature CBOR byte string member of instances of COSE_Signature type specified in IETF RFC 9052 [2], clause 4.1. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.4 The valData CBOR map | Semantics The valData CBOR map shall contain the certificates identified in 1) below, or the revocation data identified in 2) below, or both of them: 1) Certificate values that are used for validating any digital signature present within any component of the CB-AdES signature regardless the objects that they are signing (these can be, for instance, the COSE signature value within the signature component itself, any counter signature of the CB-AdES signature, or the digital signatures within any time-stamp token, attribute certificate, signed assertion, OCSP response, or CRL, or any other digital signature), without any restrictions. 2) Revocation value(s) of the certificate(s) supporting any signature present within any component of the CB-AdES signature mentioned in the previous bullet. NOTE 1: This CBOR map allows mimicking, within CB-AdES, features already incorporated in PAdES and CAdES, namely: an unsigned component whose purpose is to contain certificates and validation material that can be used for validating any signature present within CB-AdES signatures, regardless what these signatures are signing. NOTE 2: This CBOR map also allows for properly dealing with situations where different creation/validation/augmentation signature policies can be used. They, for instance, may establish different requirements on acceptable freshness of revocation material, and also allow different certificate paths. Therefore, a certain set of revocation data fully acceptable for a certain policy A, may be unacceptable, from the point of view of its freshness, for another policy B. Also a verifier can accept a different certificate path. This unsigned component allows, for instance, including within a CB-AdES signature a set of revocation data whose freshness is acceptable for this last policy B, making the signature valid under both policies. Syntax Below follows the CDDL definition of the valData CBOR map: valData = { ? 1 => xVals, ;xVals: DER-encoded encapsulated X.509 certificates or other ;encapsulated certificates ? 2 => rVals ;rVals: validation material } xVals = [ +X509OrOther ] X509OrOther = { 1 => pkiOb, // ; x509Cert: DER-encoded X.509 certificate encapsulated in an instance ;of pkiOb type ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 32 2 => pkiOb ; otherCert: Other type of certificate encoded and encapsulated in an instance ;of pkiOb type } rVals = { ? 1 => [+pkiOb], ;crlVals: array of CRLs encapsulated in an instance of pkiOb type ? 2 => [+pkiOb], ;ocspVals: array of DER-encoded OCSPResponse encapsulated in an instance ;of pkiOb type ? 3 => [+pkiOb] ;otherVals: other revocation values encoded and encapsulated in ; an instance of pkiOb type } CB-AdES signatures shall not incorporate empty valData maps. xVals map shall have at least one member. An x509Cert item shall contain one DER-encoded X.509 certificate encapsulated within an instance of pkiOb type. An otherCert item is a placeholder for potential future new formats of certificates. rVals map shall have at least one member. crlVals member shall be a non-empty array of DER-encoded X.509 CRLs [13]. Each element of crlVals array shall contain one DER-encoded X.509 CRL [13] encapsulated in a CBOR byte string. If the validation data contain one or more Delta CRLs, the crlVals member shall contain the set of CRLs required to provide complete revocation lists. ocspVals member shall be a non-empty array of DER-encoded OCSP responses [14]. Each item of ocspVals array shall contain a DER-encoded instance OCSPResponse defined in IETF RFC 6960 [14], clause 4.2.1. The otherVals member provides a placeholder for other revocation information that can be used in the future. Their semantics and syntax are outside the scope of the present document. Table 10 shows the values assigned to each of the keys in the maps specified in the present clause. Table 10: Values of keys in maps or groups specified in the present clause Name Label in map xVals (in valData) 1 rVals (in valData) 2 X509Cert (in X509OrOther) 1 otherCert (in X509OrOther) 2 crlVals (in rVals) 1 ocspVals (in rVals) 2 otherVals (in rVals) 3 |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.5 The arcTst CBOR map | |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.5.1 Semantics and syntax | Semantics The arcTst CBOR map shall encapsulate electronic time-stamps computed on the COSE Payload, the protected headers map or maps (depending on the used signature structure), the COSE signature value, the externally supplied data, when present, and the uHeaders CBOR array within the unprotected headers map at the time of generating each electronic time-stamp. NOTE 1: The purpose of this CBOR map is to tackle the long-term availability and integrity of the validation material. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 33 NOTE 2: As it has been anticipated in clause 4 any header parameter different than uHeaders CBOR array present within the unprotected headers map is not protected by the time-stamps encapsulated by this CBOR map. Syntax Below follows the CDDL definition of the arcTst CBOR map: arcTst = tstContainer If the CB-AdES signature incorporates a counter signature element, all the required material for conducting the validation of the counter signature shall be incorporated into the CB-AdES signature before generating the first arcTst CBOR map. This may be done within the counter signature itself or within the containers available within the counter-signed CB-AdES signature. The contents of the counter signature element should not be changed, once it has been time-stamped by the arcTst. NOTE 3: If a counter signature element is time-stamped by the arcTst, any subsequent change of its contents (by addition of unsigned CBOR values if the counter signature is a CB-AdES signature, for instance) would make the validation of the arcTst fail. The tstContainer member shall be as specified in clause 5.4.3.3 of the present document. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.5.2 Generation and incorporation of arcTst | The steps listed below shall be performed for augmenting a CB-AdES signature by incorporation of a new arcTst CBOR map: 1) If the CB-AdES signature misses certificates and/or revocation data required for validating the signed objects present in the CB-AdES signature, then these missing certificates and/or revocation data shall be encapsulated within a new valData CBOR map. This new valData CBOR map shall be incorporated to the CB-AdES signature before generating the electronic time-stamp(s) to be encapsulated by the arcTst CBOR map. 2) Compute the message imprint for the new archive time-stamp token(s), as indicated in clause 5.3.5.3. 3) Request as many archive time-stamp token(s) as required to the corresponding time-stamp token Service Providers. 4) Build a new arcTst CBOR map, encapsulating the time-stamp token(s) issued in the previous step and wrap it. 5) Wrap the arcTst CBOR map in a CBOR byte string and incorporate it as the last element in the uHeaders CBOR array. |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.3.5.3 Computation of message-imprint for arcTst | For computing the input to the message imprint computation, indicated in step 2) in clause 5.3.5.2, the steps listed below shall be performed: 1) Initialize an empty CBOR array. 2) Add a context text string, whose value shall be either: - "Signature", if the CB-AdES signature is built on the COSE_Sign structure defined in IETF RFC 9052 [2]; or - "Signature1", if the CB-AdES signature is built on the COSE_Sign1 structure defined in IETF RFC 9052 [2]; or - the context text string corresponding to the structure of the CB-AdES signature if it is a counter signature, as specified in clause 3.3 of IETF RFC 9338 [6]. 3) Add the protected header from the body layer, encapsulated in a CBOR byte string. If the body layer does not have the protected header, add a zero-length CBOR byte string. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 34 4) If the CB-AdES signature is built on the COSE_Sign structure, then: - If the protected header map is present in the signer layer, add the protected header from the signer layer, encapsulated in a CBOR byte string. - Else if the protected header map is absent in the signer layer, add a zero-length CBOR byte string. 5) Add the externally supplied data from the application, encapsulated in a CBOR byte string. If no data is externally supplied to the application, add a zero-length CBOR byte string. 6) If the sigD header parameter is absent, then: - If the payload field is present, then add the CBOR byte string of the payload field. - Else if the payload field is absent (COSE Payload is detached, and not explicitly referenced by the sigD header parameter), then retrieve the bytes of the COSE Payload and add them encapsulated in a CBOR byte string. NOTE 1: It is out of the scope of the present document to specify how the bytes of the unreferenced detached COSE Payload are retrieved. 7) If the sigD header parameter is present, retrieve the bytes resulting from processing the contents of its pars member as specified in clause 5.2.8.2.2 of the present document, concatenate them, encapsulate them in a CBOR byte string, and add this CBOR byte string. NOTE 2: The rationale for applying the processing specified in clause 5.2.8.2.2 of the present document to the case of the mechanism identified by "http://uri.etsi.org/19152/ObjectIdByURIHash" is the fact that this is an indirect signing mechanism, i.e. based on signing digest values of data objects, instead of the data objects themselves. Time-stamping not the digest values but the retrieved data objects, protects against future weaknesses of the digest algorithms used in sigD. 8) If the CB-AdES signature is built on a version 2 counter signature defined in IETF RFC 9338 [6], add other_fields CBOR array, as defined in clause 3.3 of IETF RFC 9338 [6]. 9) Add the CBOR byte string in the signature component. 10) If the CB-AdES signature is built on the COSE_Sign structure, take the elements in the uHeaders header parameter from the signer layer in the order that they appear within uHeaders, and add them to the CBOR array. If the signer layer does not have the uHeaders header parameter, add a zero-length CBOR byte string. 11) Else if the CB-AdES signature is built on the COSE_Sign1 structure, take the elements in the uHeaders header parameter from the body layer in the order that they appear within uHeaders and add them to the CBOR array. If the body layer does not have the uHeaders header parameter, add a zero-length CBOR byte string. 12) Encode the generated CBOR array in a CBOR byte string. The CBOR byte string resulting of step 11) shall be the input to the message imprint computation. As a consequence of the previous process, for validating a time-stamp token placed within one specific arcTst CBOR map present in a CB-AdES signature as specified in the first paragraph of this clause, its message imprint shall be built as indicated in steps 1) to 12), BUT replacing 10) and 11) with the following ones: 10) If the CB-AdES signature is built on the COSE_Sign structure, take the elements in the uHeaders header parameter from the signer layer that precede (appear BEFORE) the arcTst CBOR map that contains the time-stamp token that is being validated, in the order they appear within uHeaders, and add them to the CBOR array. If uHeaders header parameter is not present, add a zero-length CBOR byte string. 11) Else if the CB-AdES signature is built on the COSE_Sign1 structure, take the elements in the uHeaders header parameter from the body layer that precede (appear BEFORE) the arcTst CBOR map that contains the time-stamp token that is being validated, in the order they appear within uHeaders, and add them to the CBOR array. If uHeaders header parameter is not present, add a zero-length CBOR byte string. ETSI ETSI TS 119 152-1 V1.1.1 (2026-03) 35 |
cfeb51771f38af3290d7c4a899c0aa1d | 119 152-1 | 5.4 Generally useful syntax |
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