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README.md
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tags:
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- mobileNetwork
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- 5G
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---
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tags:
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- mobileNetwork
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- 5G
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task_ids:
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- Forecasting
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- Anomaly Detection
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---
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# 📡 COOPER
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### Cellular Operational Observations for Performance and Evaluation Research
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**An Open Benchmark of Synthetic Mobile Network Performance Indicators for Reproducible Research**
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---
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## 🧭 Overview
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**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.
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COOPER emulates the **statistical distributions, temporal dynamics, and structural patterns** of real 5G network PM data while containing **no sensitive or operator-identifiable information**.
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The dataset is released together with a **reproducible benchmarking framework** used to evaluate synthetic data generation methods.
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---
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## 🎯 Motivation
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Access to real telecom PM/KPI data is often restricted due to:
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- Confidentiality agreements
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- Privacy regulations
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- Commercial sensitivity
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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:
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- Network monitoring research
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- KPI forecasting
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- Anomaly detection
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- AI-native network automation
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- 5G/6G performance evaluation
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---
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## 🏗 Dataset Creation Methodology
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To generate COOPER, three complementary synthetic data generation paradigms were evaluated:
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1. **Adversarial approaches**
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2. **Probabilistic models**
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3. **Model-based time-series methods**
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These were benchmarked using a **unified quantitative and qualitative evaluation framework** considering:
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- Distributional similarity
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- Temporal fidelity
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- Shape alignment
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- Discriminative performance
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- Downstream forecasting capability
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The generator demonstrating the most **balanced and consistent performance** across these criteria was selected to produce COOPER.
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---
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## 📊 Source Data Characteristics (Before Anonymization)
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The real dataset used to model the synthetic data was:
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- Fully **anonymized** to remove operator-sensitive information
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- Cleaned and standardized for consistency
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| Property | Value |
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|---------|------|
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| Radio Access Technology | 5G |
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| Number of PM Indicators | 45 |
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| Total Number of Cells | 84 |
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| Base Stations | 12 |
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| Geographic Area | ~1.35 km² |
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| Collection Period | 31 days |
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| Sampling Interval | 1 hour |
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| Data Representation | Multi-cell time series |
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A **cell** is defined as a radiating unit within a specific RAT and frequency band. Each base station may host multiple cells.
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---
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## 📡 Network Deployment Characteristics
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The modeled network includes two frequency bands and two 5G architectures:
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| Band | Architecture | Number of Cells |
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|------|-------------|----------------|
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| N28 (700 MHz) | Option 2 (Standalone) | 6 |
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| N28 (700 MHz) | Option 3 (Non-Standalone) | 48 |
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| N78 (3500 MHz) | Option 2 (Standalone) | 6 |
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| N78 (3500 MHz) | Option 3 (Non-Standalone) | 24 |
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Most cells operate in **Option 3 (NSA)** mode, reflecting a typical **EN-DC deployment** where LTE provides the control-plane anchor.
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---
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## 📈 PM Indicator Categories
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Indicators follow **3GPP TS 28.552** performance measurement definitions and are grouped into:
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### 1️⃣ Radio Resource Control (RRC) Connection
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Procedures for establishing UE radio connections and tracking active users.
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- `RRC.ConnEstabSucc`
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- `RRC.ConnEstabAtt`
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- `RRC.ConnMax`
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### 2️⃣ Mobility Management
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Handover and redirection performance across frequencies.
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- `MM.HoExeIntraFreqSucc`
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- `MM.HoExeInterFreqSuccOut`
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### 3️⃣ Channel Quality Indicator (CQI)
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Distribution of downlink channel quality reports (CQI 0–15).
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- `CARR.WBCQIDist.Bin0`
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- `CARR.WBCQIDist.Bin15`
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### 4️⃣ Throughput and Data Volume
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Traffic volume and transmission duration.
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- `ThpVolDl`
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- `ThpTimeDl`
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### 5️⃣ Availability
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Cell downtime due to failures or energy-saving mechanisms.
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- `CellUnavail.System`
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- `CellUnavail.EnergySaving`
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### 6️⃣ UE Context
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User session establishment attempts and successes.
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- `UECNTX.Est.Att`
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- `UECNTX.Est.Succ`
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---
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## 🧪 Benchmarking Framework
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COOPER is distributed with a **reproducible evaluation pipeline** that allows researchers to compare synthetic data generators using:
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- Statistical similarity metrics
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- Temporal alignment measures
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- Shape-based similarity
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- Classification distinguishability
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- Forecasting task performance
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This framework enables standardized evaluation of synthetic telecom datasets.
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---
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## 🔬 Intended Use Cases
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COOPER is suitable for:
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- Time-series forecasting research
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- Network anomaly detection
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- AI/ML benchmarking
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- Root cause analysis modeling
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- RAN performance optimization studies
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- Reproducible academic research in 5G/6G systems
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---
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## ⚠️ Limitations
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- Synthetic data cannot capture all rare real-world network behaviors
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- Some extreme events may be underrepresented
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- Vendor-specific configurations are abstracted
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COOPER should be used as a **research and benchmarking dataset**, not as a substitute for operational network data.
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---
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## 🤝 Contribution & Reproducibility
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This project promotes **open and reproducible telecom AI research**.
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Researchers are encouraged to:
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- Benchmark new generation models using the provided framework
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- Share improvements and derived datasets
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- Compare methods under the same evaluation protocol
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---
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## 📜 License
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This dataset is released for **research and educational purposes**.
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(Include specific license here, e.g., CC BY 4.0 / MIT / Apache 2.0)
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---
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## 📖 Citation
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If you use COOPER in your research, please cite:
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> *COOPER: An Open Benchmark of Synthetic Mobile Network Performance Indicators for Reproducible Research*
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(Full citation to be added)
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---
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## 👤 Acknowledgment
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Named in honor of **Martin Cooper**, whose pioneering work made modern cellular communication possible.
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