scholarly journals Subsampled Circulant Matrix Based Wideband Spectrum Sensing Using Fusion Based Recovery Algorithm

2021 ◽  
Vol 38 (4) ◽  
pp. 1201-1208
Author(s):  
T V N L Aswini ◽  
Padma K Raju ◽  
Leela B Kumari
Keyword(s):  
Spectrum Sensing ◽  
Sampling Technique ◽  
Circulant Matrix ◽  
Hardware Complexity ◽  
Secondary Users ◽  
Recovery Algorithm ◽  
Wideband Spectrum Sensing ◽  
Modulated Wideband Converter ◽  
Added Benefit ◽  
Perfect Recovery

This paper reflects the problem of wideband spectrum recovery. The demand for spectrum usage is increasing exponentially as the wireless technologies rules the world. To meet these needs, Cognitive Radio is one of the emerging technologies, which intelligently allots the spectrum to the secondary users. Since the spectrum is wideband, the capability of spectrum sensing is improved by introducing sub-nyquist sampling under compressive sensing framework. In this paper, a sub-nyquist sampling technique of Modulated Wideband Converter (MWC) is used as it possesses m-parallel channels providing fast sensing and robust structure. A circulant matrix method is used to improve the hardware complexity of MWC. Also at the reconstruction of MWC, a fusion based recovery algorithm is proposed which became an added benefit for perfect recovery of the support. The results are compared with conventional MWC in terms of support recovery, mean square error and SNR gain. Simulations proved that the proposed algorithm performs superior at low as well as high SNR with increased gain.

scholarly journals A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3011 ◽  
Author(s):  
Zhuhua Hu ◽  
Yong Bai ◽  
Mengxing Huang ◽  
Mingshan Xie ◽  
Yaochi Zhao
Keyword(s):  
Spectrum Sensing ◽  
Noise Intensity ◽  
Sampling Rate ◽  
Fusion Center ◽  
Hardware Complexity ◽  
Selection Scheme ◽  
Wideband Spectrum Sensing ◽  
Support Recovery ◽  
The Impact ◽  
Self Adaptive

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.

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