PROJECT TITLE :

Optimal Multinode Sensing in a Malicious Cognitive Radio Network

ABSTRACT:

Spectrum sensing is a necessary perform in cognitive radio systems for dynamic spectrum access. Multinode sensing could be a technique being employed in cognitive radio networks to reinforce the sensing performance using space diversity concept. The challenges in multinode spectrum sensing are the prediction of signal status in multiple frequency bands during a low signal-to-noise ratio (SNR) regime and sensing reliability. The weighted gain combining (WGC) and the equal gain combining are the 2 soft decision cooperative sensing techniques getting used frequently in literature. In this paper, we tend to introduce weighted gain cooperative sensing using differential evolution (DE) and adjusted box-plot ways to exalt the sensing reliability together with the sensing performance. The main advantage of the WGC technique using DE is that it can generate optimal weights freelance of received signal characteristics, that is an imperative condition to comprehend the system in real time. The proposed optimal cooperative sensing method with entropy and cyclic options enhances the sensing performance, and it is less severe to noise uncertainties compared with the traditional sensing methods. It will detect the low SNR signals up to -twenty four dB at desired sensing performance (Pf = 0.1 and Pd = zero.9) with a frame size of 256 and using five nodes in cooperation. It is a vital improvement for IEEE 802.22 WRAN systems, that work beneath low SNR regime.


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