Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)

Shashank Jere, Ying Wang, Ishan Aryendu, Shehadi Dayekh, Lingjia Liu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The increased flexibility and density of spectrum access in 5G New Radio (NR) has made jamming detection and classification a critical research area. To detect coexisting jamming and subtle interference, we introduce a Bayesian Inference-assisted machine learning (ML) methodology. Our methodology uses cross-layer Key Performance Indicator data collected on a Non-Standalone (NSA) 5G NR testbed to leverage supervised learning models, and is further assessed, calibrated, and revealed using Bayesian Network Model (BNM)-based inference. The models can operate on both instantaneous and sequential time-series data samples, achieving an Area under Curve above 0.954 for instantaneous models and above 0.988 for sequential models including the echo state network (ESN) from the Reservoir Computing (RC) family, across various jamming scenarios. The 180 ms instantaneous detection time allows for continuous tracking of the dynamic jamming condition due to UE mobility. Our approach serves as a validation method and a resilience enhancement tool for ML-based jamming detection while also enabling root cause identification for observed performance degradation. The introduced BNM-based inference proof-of-concept is successful in addressing 72.2% of the erroneous predictions of the RC-based sequential detection model caused by insufficient training data samples, thereby demonstrating its near real-time applicability in 5G NR and Beyond-5G networks.

Original languageEnglish
Pages (from-to)7043-7059
Number of pages17
JournalIEEE Transactions on Wireless Communications
Volume23
Issue number7
DOIs
StatePublished - 2024

Keywords

  • 5G NR
  • Bayesian network model
  • Jamming
  • O-RAN
  • causal analysis and inference
  • interference
  • machine learning
  • near real-time
  • network intrusion
  • reservoir computing

Fingerprint

Dive into the research topics of 'Bayesian Inference-Assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)'. Together they form a unique fingerprint.

Cite this