Spectrum Anomaly Detection in OFDMA Systems: Simulation Framework and Benchmark Dataset

Abstract

Wireless connectivity underpins modern society and industry, enabling critical applications such as 5G ultra-reliable low-latency communication (URLLC) for industrial automation. However, the openness of the wireless medium exposes it to spectrum anomalies, including unintentional interference and malicious jamming, which threaten communication and sensing functionalities in 5G and emerging 6G networks. Despite its importance, spectrum anomaly detection research is hindered by a lack of publicly available datasets reflecting real-world scenarios. To address this, we present a benchmark dataset for spectrum anomaly detection in orthogonal frequency-division multiplexing access (OFDMA) systems, a core technology for 5G and beyond. The dataset includes spectrograms generated across a distributed network of sensing units, covering five distinct jammer types, from simple noise to advanced pilot-aware attacks. These anomalies are simulated in an industrial factory environment using a versatile open-source framework developed and published as part of this work, enabling extensibility to new scenarios and interference types. We provide baseline evaluations for supervised and unsupervised learning methods, demonstrating the challenges posed by different jammers and highlighting areas for further research. The dataset and framework support reproducible studies and serve as a foundation for advancing spectrum anomaly detection, with applications extending to network digital twins. By bridging the gap in open dataset availability, this work empowers the research community to validate and compare advanced detection methods for resilient next-generation wireless systems.