{"title":"Unsupervised DL-based wireless signal anomaly detection","authors":"Xiangli Liu, Wei Tan, Zan Li, Junjie Zeng","doi":"10.1016/j.dsp.2025.105578","DOIUrl":null,"url":null,"abstract":"<div><div>Detecting wireless signal anomalies in non-ideal channels and complex electromagnetic environments is a particularly challenging and demanding task. Susceptible to environmental influences, wireless signal anomalies are diverse, making its anomaly detection very difficult. To improve the detection of anomalous signals in complex electromagnetic environments, a novel unsupervised model, CAAEDS (Combined Adversarial Autoencoder and Deep SVDD) is proposed, which incorporates dual input modalities: time-domain data and Power Spectral Density (PSD). CAAEDS extracts time domain data feature information using Long Short-term Memory (LSTM) and PSD data feature information using Residual Networks (ResNets). Results from experiments demonstrate that: 1) The performance of the proposed algorithm outperforms state-of-the-art algorithms. 2) Ablation studies prove that CAAEDS can overcome the shortcomings of unsupervised AAE and Deep SVDD in wireless signal anomaly detection. 3) Wireless signal datasets collected in real-world environments verify the ability of CAAEDS to adapt to the environment and detect weak anomalies in wireless signals.</div></div>","PeriodicalId":51011,"journal":{"name":"Digital Signal Processing","volume":"168 ","pages":"Article 105578"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1051200425006001","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Detecting wireless signal anomalies in non-ideal channels and complex electromagnetic environments is a particularly challenging and demanding task. Susceptible to environmental influences, wireless signal anomalies are diverse, making its anomaly detection very difficult. To improve the detection of anomalous signals in complex electromagnetic environments, a novel unsupervised model, CAAEDS (Combined Adversarial Autoencoder and Deep SVDD) is proposed, which incorporates dual input modalities: time-domain data and Power Spectral Density (PSD). CAAEDS extracts time domain data feature information using Long Short-term Memory (LSTM) and PSD data feature information using Residual Networks (ResNets). Results from experiments demonstrate that: 1) The performance of the proposed algorithm outperforms state-of-the-art algorithms. 2) Ablation studies prove that CAAEDS can overcome the shortcomings of unsupervised AAE and Deep SVDD in wireless signal anomaly detection. 3) Wireless signal datasets collected in real-world environments verify the ability of CAAEDS to adapt to the environment and detect weak anomalies in wireless signals.
期刊介绍:
Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal.
The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as:
• big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,