scholarly journals Distributed Spectrum Sensing in a Cognitive Networking Testbed

2011 ◽  
pp. 325-326 ◽  
Author(s):  
Stefan Bouckaert ◽  
Peter Van Wesemael ◽  
Jono Vanhie-Van Gerwen ◽  
Bart Jooris ◽  
Lieven Hollevoet ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Distributed Spectrum Sensing ◽  
Networking Testbed

Reputation-Based Trust Management for Distributed Spectrum Sensing

2014 ◽  
pp. 255-264 ◽  
Author(s):  
Seamus Mc Gonigle ◽  
Qian Wang ◽  
Meng Wang ◽  
Adam Taylor ◽  
Eamonn O. Nuallain
Keyword(s):  
Spectrum Sensing ◽  
Trust Management ◽  
Distributed Spectrum Sensing

Distributed Spectrum Sensing Using Low Cost Hardware

2015 ◽  
Vol 83 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Stefan Grönroos ◽  
Kristian Nybom ◽  
Jerker Björkqvist ◽  
Juhani Hallio ◽  
Jani Auranen ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Low Cost ◽  
Distributed Spectrum Sensing

Distributed Cooperative Spectrum Sensing Algorithm Based on Average Consensus

2015 ◽  
Vol 713-715 ◽  
pp. 1090-1093
Author(s):  
Yong Xiu Feng ◽  
Ai Qin Bao ◽  
Deng Yin Zhang
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
False Alarm Probability ◽  
Consensus Algorithm ◽  
Noise Effect ◽  
Distributed Spectrum Sensing ◽  
Average Consensus ◽  
Deviation Criterion ◽  
Interaction Channel

The existing distributed spectrum sensing algorithms usually assume that the information in interaction channel is totally correct and did not consider noise effect. To solve these problems, a new distributed cooperative spectrum sensing scheme based on average consensus is investigated in this paper. Based on minimum mean square deviation criterion, we design an iterative matrix suitable for consensus algorithm with considering the noise of interaction channel. Simulation results show that the proposed method achieves better detection performance under noise effect of interaction channel and outperforms conventional scheme by 11% at-5dB signal to noise ratio (SNR) and 0.1 false alarm probability.

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