Wireless Signal Identification for Secure Spectrum Sensing Based on Multi-Scale Fourier Segmented Attention Mechanism

Ziyi Yang, Yaojun Lu, Liang Zeng*, Shuai Wang, Jianping An, Zhiquan Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The rapid development of the Internet of Things (IoT) has led to exponential growth in wireless network traffic and the number of connected devices, thereby intensifying the demand for scarce spectrum resources. In this context, Wireless signal identification, a key technology in spectrum sensing, is crucial for enhancing spectrum utilization by mitigating interference and ensuring system security. In this study, we treat wireless signal identification as a time series classification task and propose a novel model based on Fourier-segmented attention. In our proposed model, instead of computing point-level attention, we extract sequence dependencies by computing segment-level attention. Moreover, we introduce a method based on the Fourier transform to determine the segment length, ensuring that each segment captures multi-scale features. Experimental results indicate that the proposed method outperforms existing models, achieving an accuracy of approximately 95% on our dataset and representing an improvement of around 1.6% in accuracy over competing approaches. Furthermore, experiments were conducted to evaluate the model’s effectiveness in detecting fake signals and its potential to enhance system security.

Original languageEnglish
JournalIEEE Internet of Things Journal
DOIs
Publication statusAccepted/In press - 2025
Externally publishedYes

Keywords

  • deep learning
  • time series analysis
  • transformer
  • Wireless signal identification

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