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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

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Front. Comput. Sci.    2024, Vol. 18 Issue (2) : 182501    https://doi.org/10.1007/s11704-022-2158-6
Networks and Communication
Sub-Nyquist sampling-based wideband spectrum sensing: a compressed power spectrum estimation approach
Jilin WANG, Yinsen HUANG, Bin WANG()
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
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Abstract

In this paper, we introduce a sub-Nyquist sampling-based receiver architecture and method for wideband spectrum sensing. Instead of recovering the original wideband analog signal, the proposed method aims to directly reconstruct the power spectrum of the wideband analog signal from sub-Nyquist samples. Note that power spectrum alone is sufficient for wideband spectrum sensing. Since only the covariance matrix of the wideband signal is needed, the proposed method, unlike compressed sensing-based methods, does not need to impose any sparsity requirement on the frequency domain. The proposed method is based on a multi-coset sampling architecture. By exploiting the inherent sampling structure, a fast compressed power spectrum estimation method whose primary computational task consists of fast Fourier transform (FFT) is proposed. Simulation results are presented to show the effectiveness of the proposed method.
Keywords wideband spectrum sensing      sub-Nyquist      multi-coset sampling      FCPSE     
Corresponding Author(s): Bin WANG   
Just Accepted Date: 07 December 2022   Issue Date: 23 March 2023
 Cite this article:   
Jilin WANG,Yinsen HUANG,Bin WANG. Sub-Nyquist sampling-based wideband spectrum sensing: a compressed power spectrum estimation approach[J]. Front. Comput. Sci., 2024, 18(2): 182501.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-2158-6
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I2/182501
Fig.1  Block diagram of the fast compressed power spectrum estimation
  
Fig.2  Compressed power spectrum estimation via proposed algorithms on the dataset “d3072M_1000pts_2chs_1.txt”. (a) Comparation of the reconstructed power spectrum; (b) ROC curve
Fig.3  Compressed power spectrum estimation via proposed algorithms on the dataset “d3072M_10000pts_3chs_1.txt”. (a) Comparation of the reconstructed power spectrum; (b) ROC curve
Fig.4  Compressed power spectrum estimation via proposed algorithms on the dataset “d3072M_30000pts_2chs_1.txt”. (a) Comparation of the reconstructed power spectrum; (b) ROC curve
Fig.5  Power spectrum reconstructed using noiseless Nyquist samples and power spectrum reconstructed via our proposed method with 1 ms noisy sub-Nyquist samples
Fig.6  ROC curve under different compression ratios
Fig.7  From top to bottom: ground truth and estimated power spectrum reconstruction under the non-sparse situation
Fig.8  The ROC of our proposed method and the time-domain method. The run times of our proposed method and the time-domain method are 0.022 s and 0.061 s, respectively
  
  
  
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