  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 critical signaling Key Performance Indicator data collected on a
Non-Standalone (NSA) 5G NR testbed to leverage supervised learning models, and
are 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, for various
jamming scenarios. Our approach not only serves as a validation method and a
resilience enhancement tool for ML-based jamming detection, but also enables
root cause identification for any 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 collected in the observation
period, thereby demonstrating its applicability in 5G NR and Beyond-5G (B5G)
network infrastructure and user devices.
