Cooperative spectrum sensing under noisy control channel for Cognitive Radio Network

Spectrum sensing is a vital phase in Cognitive Radio (CR) to identify the unutilized spectrum for improving the spectrum utilization. Cooperative sensing is being used for spectrum sensing to mitigate the effect of shadowing and fading in the channel. In cooperative sensing, the channels to be sensed by cognitive users are assumed to be noisy. Moreover, channel noise is also presents in between CR users and fusion center which reduces the cooperative sensing detection accuracy. In this paper, we studied the effect of noise in the control channel on detection probability and used forward error correction technique with convolutional encoder to mitigate the effect of control channel noise. Energy detection based on Neyman-Pearson criteria is used in each CR and sensing performance is analyzed using Monte-Carlo methods. The simulations are carried out with different signal-to-noise ratio (SNR) in the control channel with and without convolutional coding. The results reveal that the detection probability of the algorithm improves significantly with convolutional coding.