Compressive Wideband Spectrum Sensing and Signal Recovery with Unknown Multipath Channels

2022 ◽  
pp. 1-1
Author(s):  
Hongwei Wang ◽  
Jun Fang ◽  
Huiping Duan ◽  
Hongbin Li
Keyword(s):  
Spectrum Sensing ◽  
Multipath Channels ◽  
Signal Recovery ◽  
Wideband Spectrum Sensing

scholarly journals Bayesian Compressive Sampling Based Wideband Spectrum Sensing in Cognitive Radio Network using Wavelet Transform

2019 ◽  
Vol 8 (4) ◽  
pp. 1412-1419
Keyword(s):  
Wavelet Transform ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Primary User ◽  
Compressive Sampling ◽  
Signal Recovery ◽  
Radio Network ◽  
Recovery Algorithm ◽  
Wideband Spectrum Sensing

This paper deals with the implementation of sub Nyquist sampling for the efficient wideband spectrum sensing in cognitive radio network. Cognitive radio is a very promising technology in the field of wireless communication which has drastically changed the spectral dynamics through the opportunistic utilization of frequency band by the secondary users when it is not utilized by the primary users. The complexity of spectral detection strategy is reduced using the compressive sensing method. Bayesian technique is utilized in the compressive sampling to deal with uncertainty of the process and increase the speed of detection. This technique recovers the wideband signals even with few measurements via Laplace prior and Toeplitz matrix. Sparse signal recovery algorithm is used for the extraction of primary user frequency location. The condition of the detection of primary user even in the low regulated transmission from unlicensed user is been resolved in this paper through Wavelet transform. This approach enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation analysis is performed to verify the effectiveness of the proposed technique over the cognitive radio network.

scholarly journals An Active and Passive Reputation Method for Secure Wideband Spectrum Sensing Based on Blockchain

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1346
Author(s):  
Xinyu Xie ◽  
Zhuhua Hu ◽  
Min Chen ◽  
Yaochi Zhao ◽  
Yong Bai
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Sampling Rate ◽  
Radio Networks ◽  
Single Node ◽  
Decision Algorithm ◽  
Collusion Attacks ◽  
Wideband Spectrum Sensing

Spectrum is a kind of non-reproducible scarce strategic resource. A secure wideband spectrum sensing technology provides the possibility for the next generation of ultra-dense, ultra-large-capacity communications to realize the shared utilization of spectrum resources. However, for the open collaborative sensing in cognitive radio networks, the collusion attacks of malicious users greatly affect the accuracy of the sensing results and the security of the entire network. To address this problem, this paper proposes a weighted fusion decision algorithm by using the blockchain technology. The proposed algorithm divides the single-node reputation into active reputation and passive reputation. Through the proposed token threshold concept, the active reputation is set to increase the malicious cost of the node; the passive reputation of the node is determined according to the historical data and recent performance of the blockchain. The final node weight is obtained by considering both kinds of reputation. The proposed scheme can build a trust-free platform for the cognitive radio collaborative networks. Compared with the traditional equal-gain combination algorithm and the centralized sensing algorithm based on the beta reputation system, the simulation results show that the proposed algorithm can obtain reliable sensing results with a lower number of assistants and sampling rate, and can effectively resist malicious users’ collusion attacks. Therefore, the security and the accuracy of cooperative spectrum sensing can be significantly improved in cognitive radio networks.

scholarly journals Sub-Nyquist Wideband Spectrum Sensing Based on Analog to Information Converter for Cognitive Radio

2021 ◽  
Vol 182 ◽  
pp. 132-139
Author(s):  
Marwa Mashhour ◽  
Aziza I. Hussein ◽  
Hussein Sh. Mogahed
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Wideband Spectrum Sensing

A Histogram-Based Segmentation Method for Wideband Spectrum Sensing in Cognitive Radios

2013 ◽  
Vol 62 (7) ◽  
pp. 1900-1908 ◽  
Author(s):  
Doris Bao ◽  
Luca De Vito ◽  
Sergio Rapuano
Keyword(s):  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Segmentation Method ◽  
Wideband Spectrum Sensing

scholarly journals Cooperative wideband spectrum sensing with multi-bit hard decision in cognitive radio

2013 ◽  
Author(s):  
M.A. Mehdawi ◽  
N.G. Riley ◽  
M. Ammar ◽  
A. Fanan ◽  
M. Zolfaghari
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Wideband Spectrum Sensing ◽  
Hard Decision

Wideband Spectrum Sensing From Compressed Measurements Using Spectral Prior Information

2013 ◽  
Vol 61 (24) ◽  
pp. 6232-6246 ◽  
Author(s):  
Daniel Romero ◽  
Geert Leus
Keyword(s):  
Spectrum Sensing ◽  
Prior Information ◽  
Wideband Spectrum Sensing

Wideband spectrum sensing using the all-phase FFT

2011 ◽  
Author(s):  
Lian Huai ◽  
Gerald E. Sobelman ◽  
Xiaofang Zhou
Keyword(s):  
Spectrum Sensing ◽  
Wideband Spectrum Sensing
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