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The increased flexibility and density of spectrum access in 5G New Radio (NR)has made jamming detection and classification a critical research area. Todetect coexisting jamming and subtle interference, we introduce a BayesianInference-assisted machine learning (ML) methodology. Our methodology usescross-layer critical signaling Key Performance Indicator data collected on aNon-Standalone (NSA) 5G NR testbed to leverage supervised learning models, andare further assessed, calibrated, and revealed using Bayesian Network Model(BNM)-based inference. The models can operate on both instantaneous andsequential time-series data samples, achieving an Area under Curve above 0.954for instantaneous models and above 0.988 for sequential models including theecho state network (ESN) from the Reservoir Computing (RC) family, for variousjamming scenarios. Our approach not only serves as a validation method and aresilience enhancement tool for ML-based jamming detection, but also enablesroot cause identification for any observed performance degradation. Theintroduced BNM-based inference proof-of-concept is successful in addressing72.2% of the erroneous predictions of the RC-based sequential detection modelcaused by insufficient training data samples collected in the observationperiod, thereby demonstrating its applicability in 5G NR and Beyond-5G (B5G)network infrastructure and user devices.

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