Two-Stage Spectrum Sensing for Cognitive Radio Using Energy and Entropy Detection over AWGN Channel

Abstract

The numbers of wireless devices have grown dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to overcome the problem of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the presence or absence of primary user signals, many spectrum sensing techniques such as Energy Detection, Matched filter Detection, and Cyclostationary feature Detection have been developed. Among these, Energy detection has received the most attention from researchers due to its ease of implementation, fast sensing time, and low computational complexity. However, the performance of conventional energy detector degrades rapidly at low Signal-to-noise Ratio (SNR) due to its sensitivity to noise uncertainty. To mitigate noise uncertainty problem, Shannon, Tsallis, Kapurs, and Renyi entropy-based detection have been used in this study, and their performances are compared to choose the best performer. The comparison results have shown that Renyi entropy detection has a significant improvement of about 11 dB, 10 dB and 7 dB in SNR wall compared to Tsallis, Shannon and Kapurs entropy, respectively. In this thesis, two-stage spectrum sensing are proposed using Energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Also, a performance comparison among conventional energy detection, entropy-based detection, and proposed two-stage spectrum sensing over the Additive White Gaussian Noise (AWGN) channel are performed. In addition, the cooperative detection techniques are performed to further improve the performance of the proposed two stage techniques. Simulation results show that the proposed two-stage technique has a significant improvement of about 15 dB and 0.5 dB in SNR wall when compared to Conventional Energy Detection and Renyi entropy, respectively, whereas a cooperative two-stage technique with two users indicates a 2 dB performance improvements in SNR wall compared to single node two-stage techniques. To evaluate the performance of spectrum sensing, parameters such as probability of detection, probability of false alarm, probability of miss detection and receiver operating characteristics curve are used.