Moment Based Spectrum Sensing Algorithm in Cognitive Radio Network for Future of 5G

Abstract

The cognitive radio is a smart wireless communication system that is aware of its adjacent environment and under a certain approach is capable of using the current available spectrum temporarily without interfering with the primary user. Cyclostationary detection, which exploits the periodic property of communication signal statistics, absent in stationary noise, is a natural candidate for this setting. The proposed work compromises simple moment based spectrum sensing algorithm for cognitive radio networks. It is made-up that the transmitted signal samples are binary (quadrature) phase-shift keying BPSK (QPSK), Mary quadrature amplitude modulation (QAM) or continuous uniformly distributed random variables and the noise samples are independent and identically distributed circularly symmetric complex Gaussian random variables all with unknown (inadequate) variance. Based on these assumptions, the proposed work offer a simple test statistics engaging a ratio of second and fourth moments. For this statistics, suggested work will deliver analytical expressions for both probability of false alarm ( P f ) and probability of detection ( P d ) in an additive white Gaussian noise (AWGN) channel. Here, will approve the theoretical expressions through simulation program. In addition, under noise variance uncertainty, simulation results decide that the suggested moment based detector provides better detection performance compared to that of energy detector in AWGN and Rayleigh fading channels.