Improved Cyclostationary Detection based Spectrum Sensing Technique in Cognitive Radio Networks

Abstract

Cognitive Radio (CR) is an intelligent device which is aware of the environment and its changes, can automatically detect available channels in a wireless spectrum. CR has been used to limit natural resourses efficiently without any interference to Primary Users (PUs), and can coexist with licensed user. Reliable detection of Primary Users in the presence of noise is a crucial problem in Cognitive Radio Networks (CRN). To address the above issue, spectrum sensing based on Cyclostationary Feature Detection (CFD) is proposed, which can robustly detect hidden primary signals even in low Signal to Noise Ratio (SNR). Here, CFD method is used to investigate the problem of detecting vacant spectral bands. Probability of detection is more precise in CFD which accurately detects the presence of an active primary user by computing the Spectral Correlation Function (SCF) which applies only to the cyclostationary processes, whereas stationary processes do not exhibit spectral correlation density. In this regards, the parameters, such as the Probability of detection, Probability of false alarm and the Signal to Noise ratio (SNR) are used to find the vacant frequency bands. Thus this method is more reliable than traditional energy detection scheme, as CFD can perfectly distinguish primary user signal from noise, and can perform efficiently even in low SNR region and also in fading environments.