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.

翻译：第五代（5G）网络中的所有应用均依赖于稳定的射频环境，以支持移动性、自动化和连接智能中的关键任务服务。这些应用面临的有意干扰或低功率干扰威胁其可用性与可靠性，尤其是在此类攻击低于链路层可观测性时。本文研究一种轻量化、可解释且硬件高效的干扰检测方法，该方法直接基于5G同步信号块特征，采用卷积Tsetlin机实现。CTM在量化输入上构建布尔逻辑子句，支持比特级推理并可在FPGA架构上确定性部署。这些特性使得CTM非常适合5G预期的实时、资源受限的边缘环境。所提方法在真实5G测试平台上使用空口SSB数据进行了实验验证，模拟了实际下行链路条件。我们在相同的预处理和训练流程下，将CTM与卷积神经网络基准模型进行了性能比较。在真实数据集上，CTM取得了可比的检测性能（准确率91.53 ± 1.01，CNN为96.83 ± 1.19），同时训练速度快9.5倍，内存需求减少14倍（45 MB对比624 MB）。此外，我们为Zybo Z7（Zynq-7000）平台设计了一个紧凑的FPGA导向架构，并针对延迟、功耗和准确性的权衡优化，提供了三种部署配置下的资源预估（未实测）。结果表明，对于射频域干扰检测，CTM为传统深度神经网络提供了一种实用、可解释且资源高效的替代方案，使其成为边缘部署、低延迟且安全性要求高的5G应用的强有力候选技术，同时为B5G系统奠定了基础。