Optimal Entropy-based Spectrum Sensing for Cognitive Radio Networks under Severe Path Loss Conditions

Recently maritime cognitive radio network is proposed to provide high bandwidth and low communication cost for maritime users. Spectrum sensing is one of the key issues to develop cognitive radio networks. Radio propagation is one of the main differences between maritime and land environment. Traditional detectors such as matched filter, energy detector and cyclostationary detector are not robust under low signal-to-noise ratio and at high sea state conditions. To deal with maritime environmental challenges, an entropy-based spectrum sensing scheme with the optimal number of samples is presented in this paper. Since spectrum sensing is sensitive to the number of samples, the optimal number of samples has been introduced in the proposed scheme to get minimum sensing time and maximum detection probability. Results reveal that existing scheme works well for the lower sea states but failed to perform at higher sea states. Moreover, simulation results show that the entropy-based scheme is robust at higher sea states in comparison with the traditional energy detector.