Parzen window entropy based spectrum sensing in cognitive radio

• A Parzen window entropy detection technique for spectrum sensing.
• Performance is compared with energy and Shanon entropy detection method.
• In multi-node, weights are evaluated using the Differential evolution algorithm.
• Single node sensing achieved SNR wall of −−19 db at Pd = 0.9 and Pf = 0.1.
• Multi-node sensing achieved SNR wall of −−24 dB at Pd = 0.9 and Pf = 0.1.

In this paper, we propose a Parzen window entropy based spectrum sensing algorithm for enhancing the signal-to-noise ratio (SNR) wall of cognitive radio primary user detection. We compute the information entropy using a non-parametric Kernel Density Estimation (KDE) method. Single node sensing is extended to cooperative sensing using the weighted gain combining (WGC) fusion method. The weights of WGC technique are computed using a Differential Evolution(DE) algorithm and compared with the log-likelihood ratio (LLR) method. In addition, the detection performance of the proposed Parzen window entropy is compared with Shannon entropy and energy detection techniques. We consider a DVB-T signal with Additive White Gaussian Noise (AWGN) subjected to Rayleigh fading under noise uncertainty as a primary user signal for simulation. The simulation result reveals that in the case of a single node and cooperative sensing, the proposed method achieves SNR wall of −−19 dB and −−24 dB respectively at the probability of false alarm 0.1.

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