eess.SPMar 7, 2026

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

Authors: Vojtech Halenka, Mohammadreza Amini, Per-Arne Andersen, Ole-Christoffer Granmo, Burak Kantarci

Organizations: Center of Artificial Intelligence Research, University of Agder, Grimstad, Norway · School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

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 9.5×9.5\times faster and requiring 14x less memory (45MB vs.\ 624MB). 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.

Figures & tables

Fig. 1: Network Model in consideration
Fig. 2: The TM structure [ 29 ]
ParameterValue
Carrier Frequency ( fcf_{c} )632 MHz
Bandwidth35 MHz
Sampling Rate15.625 MHz
Antenna TypeWideband omnidirectional
Duration per Capture200 ms
FFT Size1024
TABLE I: Setup and acquisition parameters for data collection.
PreprocessingAccuracysTpatchmax_lit
Enhanced Otsu91.53 ± 1.012.0847710×1022
Paper Original91.47 ± 1.063.6432610×1028
Multi Level90.88 ± 1.091.067217×750
Adaptive Thresh80.20 ± 1.641.781929×930
CNN96.83 ± 1.19––––
TABLE II: Preprocessing methods: 5-fold cross-validation accuracy with CTM best found parameters
ModelAcc. [%]Train. Time [s]Infer. Time [s]Mem. [MB]
CTM91.9644.42.545.2
CNN96.813205.40.8892.1
TABLE III: Accuracy and complexity metrics of CTM vs CNN
MetricTMCNN
Training Time (s)321.693107.91
Inference Throughput (samples/s)46.2378.23
Model Memory Usage (MB)45.06623.69
TABLE IV: CTM (incl. preprocessing) vs. CNN .
Fig. 3: Contributions of individual pixels, Red - Jamming, Blue - Pure, according to the trained CTM , using literal mentions count
Fig. 4: Contributions of individual pixels, Red - Jamming, Blue - Pure, according to the trained CNN , using GradCAM
ProfileClausesLUT sSamples/s
Power256∼\sim 7.7k–15.4k∼\sim 1.0–2.5k
Latency512∼\sim 15k–31k∼\sim 2.0–4.6k
Accuracy800∼\sim 24k–48k∼\sim 1.1–2.5k
TABLE V: Projected CTM inference footprints on Z7 . LUT s use a 30–60/clause envelope, 100 MHz, stride = 1, and 80 % efficiency. No new measurements; literature-based projection only.
More in this topic

Wireless Communications

Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

See all 399 papers

Explore similar work

CardsList
May 3, 2026

Toward Resilient 5G Networks: Comparative Analysis of Federated and Centralized Learning for RF Jamming Detection

Jamming attacks are proliferating and pose a significant threat to the security of 5G and beyond networks. These attacks target 5G radio frequency (RF) domain and can disrupt the communication in wireless networks. While conventional machine learning and deep learning approaches demonstrate its potential for jamming detection, they typically require centralized data collection, compromising the privacy of user equipment (UEs). This work proposes a federated learning (FL)-based jamming detection framework that operates on over-the-air In-phase and Quadrature (IQ) samples extracted from Synchronization Signal Blocks (SSBs) in the RF domain. The framework enables collaborative model training across multiple UEs without sharing raw RF signal data. We adopt Federated Averaging (FedAvg) algorithm to train a 1D convolutional neural network (1DCNN) for effective detection of attacks. Numerical results demonstrate that the proposed FL framework achieves 97% accuracy and 97% F1-score, outperforming centralized baselines including MLP, 1DCNN, SVM, and logistic regression, while preserving the data privacy of all participating UEs

Samhita Kuili, Mohammadreza Amini, Burak Kantarci

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

Wireless CommunicationsPrivacy-Preserving MLFederated Learning
AskPDF
Oct 14, 2026

O-RAN-Native Jamming Detection Under Network Heterogeneity

Radio-frequency jamming threatens cellular network operations in contested environments. The Open Radio Access Network (O-RAN) architecture exposes per-User Equipment (UE) telemetry through the E2 interface, enabling Key Performance Indicator (KPI)-based jamming detection without proprietary hardware. However, operational deployment demands detection methods that generalize across heterogeneous networks. We propose a Statistical Ensemble Method (SEM) combining adaptive Median Absolute Deviation (MAD)-based baseline detection, count-based anomaly detection, and abrupt change detection through a weighted voting system. SEM operates in two modes: a cold-start mode requiring no labeled data for immediate deployment, and a supervised mode where parameters are refined offline via grid search as labeled data accumulates. We evaluate SEM against five machine learning (ML) classifiers and five deep learning (DL) sequence models using approximately 25 hours of testbed data comprising logs from eight heterogeneous 5G environments, employing both single-environment and multi-environment leave-one-out (LOO) validation. Our analysis indicates that under conditions not encountered during training (distribution shift), learning-based models tend to lose recall and miss jamming events, whereas SEM, even in cold-start mode, maintains detection capabilities at the cost of false alarms. Furthermore, SEM's parameter optimization can be mapped to O-RAN's Non-RT RIC as an rApp for continuous refinement, allowing operators to transition to learning-based alternatives once corpus coverage is sufficient.

Sneihil Gopal, Fernando J. Cintrón, Richard A. Rouil

PREP Associate, National Institute of Standards and Technology (NIST), USA · Department of Physics, Georgetown University, USA · National Institute of Standards and Technology (NIST), USA

Wireless CommunicationsAnomaly DetectionOpen Radio Access Network
AskPDF
Mar 3, 2026

Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems

With the increasing computational demands of deep neural network (DNN) inference on resource-constrained devices, DNN partitioning-based device-edge collaborative inference has emerged as a promising paradigm. However, the transmission of intermediate feature data is vulnerable to malicious jamming, which significantly degrades the overall inference performance. To counter this threat, this letter focuses on an anti-jamming collaborative inference system in the presence of a malicious jammer. In this system, a DNN model is partitioned into two distinct segments, which are executed by wireless devices and edge servers, respectively. We first analyze the effects of jamming and DNN partitioning on inference accuracy via data regression. Based on this, our objective is to maximize the system's revenue of delay and accuracy (RDA) under inference accuracy and computing resource constraints by jointly optimizing computation resource allocation, devices' transmit power, and DNN partitioning. To address the mixed-integer nonlinear programming problem, we propose an efficient alternating optimization-based algorithm, which decomposes the problem into three subproblems that are solved via Karush-Kuhn-Tucker conditions, convex optimization methods, and a quantum genetic algorithm, respectively. Extensive simulations demonstrate that our proposed scheme outperforms baselines in terms of RDA.

Mengru Wu, Jiawei Li, Jiaqi Wei +3

College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China · School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China · Department of Electronic and Electrical Engineering, University College London, WC1E 7JE London, U.K. +2

Distributed InferenceEdge ComputingWireless Resource AllocationWireless Communications
AskPDF