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Interference Anomaly Detection Dataset
Interference Anomaly Detection Dataset

Interference Anomaly Detection Dataset

Mohamed Ali Msadek

This dataset provides 1-second–granularity time-series measurements collected from a dedicated 5G Standalone (SA) testbed operating on band n41. It includes multi-layer RAN telemetry (MAC, RLC, PDCP) under both nominal conditions and intentionally induced persistent uplink interference, enabling reproducible anomaly detection, cross-layer analysis, and machine learning research.

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Detailed Description

This dataset is generated from reproducible indoor laboratory experiments conducted in a Faraday cage using a 5G SA deployment on band n41. The setup includes a USRP B210 connected to a gNB and a stationary UE (Quectel RM520N-GL) under line-of-sight conditions. Persistent uplink interference is intentionally introduced using a secondary UE and gNB operating on the same frequency band with a different PLMN. This controlled configuration enables clear separation between normal and interference conditions without affecting core network procedures. Measurements are recorded at 1-second granularity over approximately two hours and include synchronized MAC, RLC, and PDCP layer statistics. A binary label indicates the presence of sustained interference, supporting supervised and unsupervised anomaly detection research.

Key Features

  • 1-second–granularity time-series RAN measurements
  • Reproducible nominal and interference conditions
  • Suitable for time-series analysis and deep anomaly detection models

Use Cases

  • Uplink interference anomaly detection
  • Cross-layer RAN performance analysis
  • Supervised vs. unsupervised anomaly detection benchmarking
  • LSTM-AE and deep sequential model evaluation
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External Resources

Persistent Interference Anomaly Detection Dataset README

detailed dataset documentation
View Details

Technical Details

Version1.0.0
Published01 May, 2025
Base
Platform:
BubbleRAN MX-PDK
Data Format:
CSV
Network:
5G Standalone
Hardware:
USRP B210, Quectel RM520N-GL
LicenseBubbleRAN Datasets License

Author

Mohamed Ali Msadek
Mohamed Ali Msadek

Tags

  • O-RAN
  • Monitor
  • Machine Learning
  • Artificial Intelligent
  • OpenAirInterface

Affiliation

  • BubbleRAN
  • EURECOM

Certified By

  • BubbleRANBubbleRAN

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