ConvLSTM-Based Spectrum Sensing at Very Low SNR

被引：8

作者：

Wang, Qian ^{[1
]}

Su, Bo ^{[1
]}

Wang, Chenxi ^{[1
]}

Qian, Li Ping ^{[1
]}

Wu, Yuan ^{[2
,3
]}

Yang, Xiaoniu ^{[4
,5
]}

机构：

[1] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Peoples R China

[2] Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China

[3] Univ Macau, Dept Comp & Informat Sci, Macau, Peoples R China

[4] Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou 310023, Peoples R China

[5] Sci & Technol Commun Informat Secur Control Lab, Jiaxing 314033, Peoples R China

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Spectrum sensing; low-SNR; deep learning; ConvLSTM; COGNITIVE RADIO; OPTIMIZATION; ALGORITHMS;

D O I：

10.1109/LWC.2023.3254048

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Spectrum sensing can effectively improve the spectrum utilization. In practice, it is difficult to sense whether the spectrum is occupied or not due to the low signal energy at very low signal-to-noise ratio (SNR) (e.g., -20dB). To overcome this issue, this letter considers the correlation of the time-frequency domains, and proposes a ConvLSTM based spectrum sensing method. To be specific, we first apply the ConvLSTM network to extract the temporal and spatial features of the sensed IQ signals simultaneously, and then realize the low-SNR spectrum sensing according to the extracted features. Simulation results show that our proposed method can reduce the sensing error by about 25%, in comparison with other deep learning based spectrum sensing methods when the SNR is -20dB.

引用

页码：967 / 971

页数：5

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