Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine

2026-03-07

AI Summary

This work addresses the reliability challenges of critical 5G services under low-power interference or covert jamming attacks, particularly when the jamming signal falls below the link-layer detection threshold. The study introduces the Convolutional Tsetlin Machine (CTM) into the 5G radio frequency domain for the first time, enabling lightweight, interpretable, and hardware-efficient jamming detection through direct Boolean logic modeling of quantized Synchronization Signal Blocks (SSBs). A targeted optimization on the Zybo Z7 FPGA platform facilitates bit-level inference and deterministic deployment. Experimental results on a real-world 5G testbed demonstrate a detection accuracy of 91.53%, with training speed 9.5× faster than CNNs and a memory footprint of only 45 MB—approximately one-fourteenth that of CNNs. The approach further offers three deployment configurations to balance latency, power consumption, and accuracy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

All applications in fifth-generation (5G) networks rely on stable radio-frequency (RF) environments to support mission-critical services in mobility, automation, and connected intelligence. Their exposure to intentional interference or low-power jamming threatens availability and reliability, especially when such attacks remain below link-layer observability. This paper investigates lightweight, explainable, and hardware-efficient jamming detection using the Convolutional Tsetlin Machine (CTM) operating directly on 5G Synchronization Signal Block (SSB) features. CTM formulates Boolean logic clauses over quantized inputs, enabling bit-level inference and deterministic deployment on FPGA fabrics. These properties make CTM well suited for real-time, resource-constrained edge environments anticipated in 5G. The proposed approach is experimentally validated on a real 5G testbed using over-the-air SSB data, emulating practical downlink conditions. We benchmark CTM against a convolutional neural network (CNN) baseline under identical preprocessing and training pipelines. On the real dataset, CTM achieves comparable detection performance (Accuracy 91.53 +/- 1.01 vs. 96.83 +/- 1.19 for CNN) while training $9.5\times$ faster and requiring 14x less memory (45~MB vs.\ 624~MB). Furthermore, we outline a compact FPGA-oriented design for Zybo~Z7 (Zynq-7000) and provide resource projections (not measured) under three deployment profiles optimized for latency, power, and accuracy trade-offs. The results show that the CTM provides a practical, interpretable, and resource-efficient alternative to conventional DNNs for RF-domain jamming detection, establishing it as a strong candidate for edge-deployed, low-latency, and security-critical 5G applications while laying the groundwork for B5G systems.

Problem

• jamming detection
• 5G networks
• explainable AI
• hardware efficiency
• RF interference

Innovation

• Convolutional Tsetlin Machine
• Explainable AI
• Hardware-efficient
• 5G jamming detection
• FPGA deployment