--- license: apache-2.0 language: - en tags: - mobileNetwork - 5G task_ids: - univariate-time-series-forecasting - multivariate-time-series-forecasting configs: - config_name: measurements_by_cell data_files: - split: train path: dataset/train_data.csv - split: test path: dataset/test_data.csv - config_name: topology data_files: - split: main path: metadata/topology.csv - config_name: performance_indicators_meanings data_files: - split: main path: metadata/performance_indicators_meanings.csv --- # ๐Ÿ“ก COOPER ### Cellular Operational Observations for Performance and Evaluation Research **An Open Benchmark of Synthetic Mobile Network Performance Indicators for Reproducible Research** --- ## ๐Ÿงญ Overview **COOPER** is an open-source **synthetic dataset of mobile network performance measurement (PM) time series**, designed to support **reproducible AI/ML research** in wireless networks. The dataset is named in honor of **Martin Cooper**, a pioneer of cellular communications. COOPER emulates the **statistical distributions, temporal dynamics, and structural patterns** of real 5G network PM data while containing **no sensitive or operator-identifiable information**. The dataset is released together with a **reproducible benchmarking framework** used to evaluate synthetic data generation methods. --- ## ๐ŸŽฏ Motivation Access to real telecom PM/KPI data is often restricted due to: - Confidentiality agreements - Privacy regulations - Commercial sensitivity This lack of open data limits **reproducibility** in AI-driven research for wireless networks. COOPER addresses this gap by providing a **realistic yet privacy-preserving synthetic alternative** suitable for: - Network monitoring research - KPI forecasting - Anomaly detection - AI-native network automation - 5G/6G performance evaluation --- ## ๐Ÿ— Dataset Creation Methodology To generate COOPER, three complementary synthetic data generation paradigms were evaluated: 1. **Adversarial approaches** 2. **Probabilistic models** 3. **Model-based time-series methods** These were benchmarked using a **unified quantitative and qualitative evaluation framework** considering: - Distributional similarity - Temporal fidelity - Shape alignment - Discriminative performance - Downstream forecasting capability The generator demonstrating the most **balanced and consistent performance** across these criteria was selected to produce COOPER. --- ## ๐Ÿ“Š Source Data Characteristics (Before Anonymization) The real dataset used to model the synthetic data was: - Fully **anonymized** to remove operator-sensitive information - Cleaned and standardized for consistency | Property | Value | |---------|------| | Radio Access Technology | 5G | | Number of PM Indicators | 45 | | Total Number of Cells | 84 | | Base Stations | 12 | | Geographic Area | ~1.35 kmยฒ | | Collection Period | 31 days | | Sampling Interval | 1 hour | | Data Representation | Multi-cell time series | A **cell** is defined as a radiating unit within a specific RAT and frequency band. Each base station may host multiple cells. --- ## ๐Ÿ“ก Network Deployment Characteristics The modeled network includes two frequency bands and two 5G architectures: | Band | Architecture | Number of Cells | |------|-------------|----------------| | N28 (700 MHz) | Option 2 (Standalone) | 6 | | N28 (700 MHz) | Option 3 (Non-Standalone) | 48 | | N78 (3500 MHz) | Option 2 (Standalone) | 6 | | N78 (3500 MHz) | Option 3 (Non-Standalone) | 24 | Most cells operate in **Option 3 (NSA)** mode, reflecting a typical **EN-DC deployment** where LTE provides the control-plane anchor. --- ## ๐Ÿ“ˆ PM Indicator Categories Indicators follow **3GPP TS 28.552** performance measurement definitions and are grouped into: ### 1๏ธโƒฃ Radio Resource Control (RRC) Connection Procedures for establishing UE radio connections and tracking active users. - `RRC.ConnEstabSucc` - `RRC.ConnEstabAtt` - `RRC.ConnMax` ### 2๏ธโƒฃ Mobility Management Handover and redirection performance across frequencies. - `MM.HoExeIntraFreqSucc` - `MM.HoExeInterFreqSuccOut` ### 3๏ธโƒฃ Channel Quality Indicator (CQI) Distribution of downlink channel quality reports (CQI 0โ€“15). - `CARR.WBCQIDist.Bin0` - `CARR.WBCQIDist.Bin15` ### 4๏ธโƒฃ Throughput and Data Volume Traffic volume and transmission duration. - `ThpVolDl` - `ThpTimeDl` ### 5๏ธโƒฃ Availability Cell downtime due to failures or energy-saving mechanisms. - `CellUnavail.System` - `CellUnavail.EnergySaving` ### 6๏ธโƒฃ UE Context User session establishment attempts and successes. - `UECNTX.Est.Att` - `UECNTX.Est.Succ` --- ## ๐Ÿงช Benchmarking Framework COOPER is distributed with a **reproducible evaluation pipeline** that allows researchers to compare synthetic data generators using: - Statistical similarity metrics - Temporal alignment measures - Shape-based similarity - Classification distinguishability - Forecasting task performance This framework enables standardized evaluation of synthetic telecom datasets. --- ## ๐Ÿ”ฌ Intended Use Cases COOPER is suitable for: - Time-series forecasting research - Network anomaly detection - Root cause analysis modeling - RAN performance optimization studies - Reproducible academic research in 5G/6G systems --- ## โš ๏ธ Data Notice for Dataset Users **Due to the real network nature of the source data, some inconsistent values were intentionally maintained in this dataset.** We recommend **preprocessing the data before use** (e.g., handling outliers, missing values, or domain-specific inconsistencies) according to your application and methodology. --- ## ๐Ÿค Contribution & Reproducibility This project promotes **open and reproducible telecom AI research**. Researchers are encouraged to: - Benchmark new generation models using the provided framework - Share improvements and derived datasets - Compare methods under the same evaluation protocol --- ## ๐Ÿ“œ License This dataset is released for **research and educational purposes**. (Include specific license here, e.g., CC BY 4.0 / MIT / Apache 2.0) --- ## ๐Ÿ“– Citation If you use COOPER in your research, please cite: > *COOPER: An Open Benchmark of Synthetic Mobile Network Performance Indicators for Reproducible Research* (Full citation to be added)