用轻量可解释模型实现实时5G干扰检测，效率远超传统神经网络。
Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine
- 基于卷积型命题机（CTM）直接处理5G同步信号特征，实现低功耗推理。
- 在真实测试平台下准确率达91.53%，训练速度比CNN快9.5倍，内存占用仅1/14。
- 模型逻辑可解释，适合部署于边缘设备，适用于安全关键的5G/B5G系统。
第五代（5G）网络中所有应用依赖稳定的射频（RF）环境，以支持移动性、自动化和智能互联等关键任务服务。其易受有意干扰或低功率干扰威胁，尤其当攻击低于链路层可观测性时。本文提出一种轻量、可解释且硬件高效的干扰检测方法，采用卷积型命题机（CTM）直接处理5G同步信号块（SSB）特征。CTM在量化输入上构建布尔逻辑条款，支持位级推断，可在FPGA上确定性部署，适合资源受限的5G边缘环境。该方法在真实5G测试平台使用空中传输的SSB数据进行验证，模拟实际下行条件。与卷积神经网络（CNN）基线在相同预处理和训练流程下对比：在真实数据集上，CTM准确率91.53%±1.01，接近CNN的96.83%±1.19，但训练速度快9.5倍，内存需求仅45~MB，远低于CNN的624~MB。此外，针对Zybo Z7（Zynq-7000）平台设计了紧凑的FPGA架构，并给出三种优化延迟、功耗和精度权衡的部署方案资源预测。结果表明，CTM为射频域干扰检测提供了一种实用、可解释且资源高效的选择，是边缘部署、低延迟、高安全性的5G应用的理想候选，也为未来6G系统奠定基础。
原文摘要 · Abstract (English)
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.
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