Multinode sensing with forward error correction and differential evolution algorithms for noisy cognitive radio networks

Abstract

Spectrum sensing is a vital phase in Cognitive Radio (CR) to identify the unutilized spectrum for dynamic spectrum access (DSA). Cooperative/multinode sensing is being used for signal detection to achieve spatial diversity gains. In cooperative sensing, the sensing channels are assumed to be noisy. However, the reporting/control channels (channels between CR and fusion center) are also contaminate with the noise, which degrades the cooperative detection accuracy. In this paper, (i) effect of reporting channels are studied and used forward error correction technique (convolutional encoder) to mitigate the effect of reporting channel noise, (ii) differential evolution algorithm is used to evaluate the optimum weights for cooperative users to maximize the sensing performance. Three different detection methods are considered for performance analysis. The simulations are carried out with different signal-to-noise ratio in control channel with and without error correction. The results reveal that, detection probability and accuracy of the system can be improved with convolutional coding together with differential evolution algorithm. © 2013 Elsevier Ltd. All rights reserved.