Cyclostationary Based Spectrum Sensing In Cognitive Radio Networks With Rls Filter

Abstract

Demand of Radio Frequency (RF) spectrum is increasing to meet the requirement of the users in wireless communication. A cognitive radio (CR) is a dynamic programmable  device to utilize the unoccupied channels in the chosen spectrum of interest. Detection of primary user is essential to choose the unoccupied channel for the secondary cognitive radio user without disturbing the access of the primary user. Major narrow band techniques involved in spectrum sensing are Energy detection, Matched filter detection and Cyclostationary based feature detection. In this paper, an adaptive RLS filter based cyclostationary feature detection with Spectral autocorrelation function is proposed. With the inclusion of the adaptive Recursive Least Square (RLS) filter in spectrum sensing, a better detection of primary user is achieved at a very low value of SNR of the primary user. It also enables the CR to track changes in the spectrum occupancy at a faster rate even at low SNR of primary user and allows more accurate detection. The spectrum sensing  techniques are simulated and analyzed in MATLAB platform. Simulation results show that the proposed method approaches maximum probability of detection at 5dB SNR with a probability of false alarm ranges from 10^-1 to 10^-5, whereas existing methods needs higher SNR value to achieve the  maximum detection of primary user and that leads to more power consumption.