Spectrum Sensing under Unknown Channel Division

Yirui Du, Bin Li, Xin Jiang, Ruonan Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent spectrum sensing technologies, deep learning methods are receiving increasing attention, mainly due to their ability to effectively improve sensing accuracy in low signal-to-noise ratio (SNR) environments. However, most of the existing deep learning technologies rely on known channel divisions for spectrum sensing, which is often difficult to achieve in real-world applications. To address this issue, this study proposes two innovative spectrum sensing methods: multi-scale serial networks and multi-scale parallel networks. Compared to traditional methods, these two networks can perform spectrum sensing without prior knowledge of channel division, significantly improving the recognition ability of wireless signals and spectrum utilization. Simulation results show that the proposed serial and parallel networks achieve higher sensing accuracy compared to traditional CNN networks under unknown channel division conditions.

Original languageEnglish
Title of host publicationProceedings - 2024 3rd International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages222-226
Number of pages5
ISBN (Electronic)9798331528386
DOIs
StatePublished - 2024
Event3rd International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024 - Zhuhai, China
Duration: 25 Oct 202427 Oct 2024

Publication series

NameProceedings - 2024 3rd International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024

Conference

Conference3rd International Conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2024
Country/TerritoryChina
CityZhuhai
Period25/10/2427/10/24

Keywords

  • Spectrum sensing
  • channel division
  • deep learning
  • multi-scale

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