Double Threshold Feature Detector for Cooperative Spectrum Sensing in Cognitive Radio Networks

From the last decade much more emphasis were drawn to develop robust Cognitive Radio Networks (CRNs). For robust CRNs, Spectrum Sensing is one of the most important issue for its better existence in licensed spectrum. Spectrum sensing is used for finding unoccupied frequency bands, called white space or spectrum holes. However, spectrum sensing performance in practice is often compromised with multipath fading, shadowing and receiver uncertainty issues due to low signal to noise ratio (SNR). In this paper, we propose a robust double threshold feature detector (DTFD) that detect even very low SNR primary user. Here we have used DTFD in cooperative spectrum sensing scenario in cognitive radio networks (CRNs) on a additive white Gaussian noise (AWGN) channel. Here individual detectors employ double threshold method in feature based detection of Orthogonal Frequency Division Multiplexing (OFDM) signal, and sends their results to fusion center (FC) where final decision takes place. Detection is done by using popular autocorrelation property of cyclic prefix (CP) in OFDM signal. From proposed technique, our objective is to achieve a better sensing performance under very low SNR, such that the primary networks and the CRNs can coexist in better way. Simulation results are done under theoretical perspective which illustrate that the proposed sensing technique can reliably detect OFDM signals at SNR as low as -20 dB.