Wideband Spectrum Sensing in Cognitive Radio with Double-Level Binary Tree Bayesian Compressed Sensing

2016 ◽  
Author(s):  
Yongqing Qian ◽  
Weizhen Chen
Keyword(s):  
Cognitive Radio ◽  
Compressed Sensing ◽  
Spectrum Sensing ◽  
Binary Tree ◽  
Wideband Spectrum Sensing

scholarly journals Wideband Cognitive Radio based on Scheduled Sequential Compressed Spectrum Sensing

2019 ◽  
Vol 8 (2) ◽  
pp. 4691-4695
Keyword(s):  
Big Data ◽  
Cognitive Radio ◽  
Compressed Sensing ◽  
Compressive Sensing ◽  
Spectrum Sensing ◽  
Low Snr ◽  
Wideband Spectrum Sensing ◽  
Big Data Applications ◽  
Wide Range ◽  
Successive Location

The cooperation for big data applications through the cognitive radio innovation requires wideband spectrum sensing. Conversely, it is expensive to employ long haul wideband detecting and is particularly troublesome within the sight of vulnerability. For example, more noise, obstruction, anomalies, as well as channel blurring. In this article, we project the planning of successive compacted range detecting which together endeavors compressive sensing (CS) and consecutive occasional identification procedures to accomplish increasingly exact and convenient wideband detecting. Rather than summoning CS to recreate the signal in every period, our projected plan executes in reverse assembled packed information consecutive likelihood proportion test (in reverse GCD-SPRT) utilizing compacted information tests in successive identification, while CS recuperation is just sought after when required. This technique altogether diminishes the compressed sensing recuperation overhead, and on different exploits successive location to increase the detecting excellence. Moreover, we project an inside and out detecting plan to quicken detecting basic leadership when an adjustment in channel position is suspicious, (b) a square scanty CS remaking calculation to abuse the square sparsityfeatures of wide range, and (c) a lot of plans to meld results from the recuperated range signs to additionally improve the general detecting exactness. Broad execution assessment results demonstrate that the projected plans can altogether outflank peer conspires below adequately low SNR properties.

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 rapid coarse-grained blind wideband spectrum sensing method for cognitive radio networks

2021 ◽  
Vol 166 ◽  
pp. 234-243
Author(s):  
Peng Feng ◽  
Yuebin Bai ◽  
Yuhao Gu ◽  
Jun Huang ◽  
Xiaolin Wang ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Coarse Grained ◽  
Wideband Spectrum Sensing

scholarly journals A Linear Weighted Sum Multi-objective Optimization Algorithm Based on PSO for Wideband Spectrum Sensing

2015 ◽  
Vol 11 (9) ◽  
pp. 9 ◽  
Author(s):  
Yonghua Wang ◽  
Yuehong Li ◽  
Yiquan Zheng ◽  
Ting Liang ◽  
Yuli Fu
Keyword(s):  
Particle Swarm Optimization ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Weighted Sum ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cognitive Radio Sensor Networks ◽  
Wideband Spectrum Sensing

In order to maximize throughput and minimize interference of the wideband spectrum sensing problem in OFDM cognitive radio sensor networks, a linear weighted sum multi-objective algorithm based on the Particle Swarm Optimization is proposed. The multi-objective optimization advantages of Particle Swarm Optimization are utilized to solve the optimal threshold vector of the spectrum sensing problem in OFDM cognitive radio sensor networks. So the network can get a larger throughput under the condition of small interference. The simulation results show that the proposed algorithm can make larger throughput while keeping the interference is smaller in OFDM cognitive radio sensor networks. Thus the spectrum resources are used more effectively.

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