Cyclostationary spectrum sensing based partial QR decomposition at low SNR regimes

Author(s):  
Shaoka Sun ◽  
Hai Huang ◽  
Xiao Jun Jing
2019 ◽  
Vol 26 (7) ◽  
pp. 991-995 ◽  
Author(s):  
Chaochao Sun ◽  
Peizhong Lu ◽  
Kai Cao

2017 ◽  
Vol 67 (3) ◽  
pp. 325 ◽  
Author(s):  
Chhagan Charan ◽  
Rajoo Pandey

<p>A novel adaptive threshold spectrum sensing technique based on the covariance matrix of received signal samples is proposed. The adaptive threshold in terms of signal to noise ratio (SNR) and spectrum utilisation ratio of primary user is derived. It considers both the probability of detection and the probability false alarm to minimise the overall decision error probability. The energy- based spectrum sensing scheme shows high vulnerability under noise uncertainty and low SNR. The existing covariance-based spectrum sensing technique overcomes the noise uncertainty problem but its performance deteriorates under low SNR. The proposed covariance-based scheme effectively addresses the low SNR problem. The superior performance of this scheme over the existing covariance-based detection method is confirmed by the simulation results in terms of probability of detection, probability of error, and requirement of samples for reliable detection of spectrum.</p>


2011 ◽  
Vol 66 (4) ◽  
pp. 751-770
Author(s):  
Cong Wang ◽  
Xianbin Wang ◽  
Hao Li ◽  
Paul Ho

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 841 ◽  
Author(s):  
Di He ◽  
Xin Chen ◽  
Ling Pei ◽  
Lingge Jiang ◽  
Wenxian Yu

Noise uncertainty and signal-to-noise ratio (SNR) wall are two very serious problems in spectrum sensing of cognitive radio (CR) networks, which restrict the applications of some conventional spectrum sensing methods especially under low SNR circumstances. In this study, an optimal dynamic stochastic resonance (SR) processing method is introduced to improve the SNR of the receiving signal under certain conditions. By using the proposed method, the SNR wall can be enhanced and the sampling complexity can be reduced, accordingly the noise uncertainty of the received signal can also be decreased. Based on the well-studied overdamped bistable SR system, the theoretical analyses and the computer simulations verify the effectiveness of the proposed approach. It can extend the application scenes of the conventional energy detection especially under some serious wireless conditions especially low SNR circumstances such as deep wireless signal fading, signal shadowing and multipath fading.


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