Parzen window entropy based spectrum sensing in cognitive radio. (May 2016)

• Record Type: Journal Article
• Authors: Swetha, N.; Sastry, Panyam Narahari; Rao, Y. Rajasree; Sabat, Samrat L.
• Is Part Of: Computers & electrical engineering. Volume 52(2016)
• Page Start: 379
• Page End: 389
• Publication Date: 2016-05
• Subjects: Non-parametric kernel -- Parzen entropy -- Shannon entropy -- Renyi entropy -- Differential Evolution -- Log-likelihood ratio; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.02.002
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: 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. Graphical abstract: Abstract: 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.