| --- |
| 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 |
|
|
|
|
|
|
|
|
| --- |
| # 📡 COOPER |
| ### Cellular Operational Observations for Performance and Evaluation Research |
| **An Open Benchmark of Synthetic Mobile Network Performance Indicators for Reproducible Research** |
|
|
| --- |
|
|
| ## 🧭 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. |
|
|
| --- |
|
|
| ## 🎯 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 |
|
|
| --- |
|
|
| ## 🏗 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. |
|
|
| --- |
|
|
| ## 📊 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 km² | |
| | 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. |
|
|
| --- |
|
|
| ## 📡 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. |
|
|
| --- |
|
|
| ## 📈 PM Indicator Categories |
|
|
| Indicators follow **3GPP TS 28.552** performance measurement definitions and are grouped into: |
|
|
| ### 1️⃣ Radio Resource Control (RRC) Connection |
| Procedures for establishing UE radio connections and tracking active users. |
| - `RRC.ConnEstabSucc` |
| - `RRC.ConnEstabAtt` |
| - `RRC.ConnMax` |
|
|
| ### 2️⃣ Mobility Management |
| Handover and redirection performance across frequencies. |
| - `MM.HoExeIntraFreqSucc` |
| - `MM.HoExeInterFreqSuccOut` |
|
|
| ### 3️⃣ Channel Quality Indicator (CQI) |
| Distribution of downlink channel quality reports (CQI 0–15). |
| - `CARR.WBCQIDist.Bin0` |
| - `CARR.WBCQIDist.Bin15` |
|
|
| ### 4️⃣ Throughput and Data Volume |
| Traffic volume and transmission duration. |
| - `ThpVolDl` |
| - `ThpTimeDl` |
|
|
| ### 5️⃣ Availability |
| Cell downtime due to failures or energy-saving mechanisms. |
| - `CellUnavail.System` |
| - `CellUnavail.EnergySaving` |
|
|
| ### 6️⃣ UE Context |
| User session establishment attempts and successes. |
| - `UECNTX.Est.Att` |
| - `UECNTX.Est.Succ` |
|
|
| --- |
|
|
| ## 🧪 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. |
|
|
| --- |
|
|
| ## 🔬 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 |
|
|
| --- |
|
|
| ## ⚠️ 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. |
|
|
| --- |
|
|
| ## 🤝 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 |
|
|
| --- |
|
|
| ## 📜 License |
|
|
| This dataset is released for **research and educational purposes**. |
| (Include specific license here, e.g., CC BY 4.0 / MIT / Apache 2.0) |
|
|
| --- |
|
|
| ## 📖 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) |
|
|
|
|
