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

arXiv: https://arxiv.org/abs/2603.07336 · HTML: https://mlsanity.com/paper/2603.07336/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [The Convolutional Tsetlin Machine](https://mlsanity.com/paper/1905.09688/) (2019)
- [Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey](https://mlsanity.com/paper/2101.00292/) (2021)
- [5G NR Jamming, Spoofing, and Sniffing: Threat Assessment and Mitigation](https://mlsanity.com/paper/1803.03845/) (2018)
- [Bayesian Inference-assisted Machine Learning for Near Real-Time Jamming Detection and Classification in 5G New Radio (NR)](https://mlsanity.com/paper/2304.13660/) (2023)
- [ETHEREAL: Energy-efficient and High-throughput Inference using Compressed Tsetlin Machine](https://mlsanity.com/paper/2502.05640/) (2025)
- [An All-digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator](https://mlsanity.com/paper/2501.19347/) (2025)
- [Jamming Detection With Subcarrier Blanking for 5G and Beyond in Industry 4.0 Scenarios](https://mlsanity.com/paper/2106.07970/) (2021)
- [Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis](https://mlsanity.com/paper/2311.17097/) (2023)
- [DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs](https://mlsanity.com/paper/2403.02645/) (2024)
- [The impact of mobility, beam sweeping and smart jammers on security vulnerabilities of 5G cells](https://mlsanity.com/paper/2411.05131/) (2024)

## Abstract (arXiv)

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× 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.