A collaborative spectrum sensing algorithm for cognitive radio based on related vector machine

Abstract

Due to the presence of tall buildings, mountains and other high occlusions in mountainous cities, this will produce fading phenomena, which will result in weak or even unrecognizable signals from the main users. To address this problem, a Related Vector Machine (RVM) based spectrum sensing method is proposed in this paper. First, the cognitive radio users (CR users) selection mechanism based on location correlation is designed, and some CR users with the best sensing performance are selected to participate in the sensing of the primary user (PU). Second, some parameters that reflect the characteristics of the PU signal are selected as the sample parameters. Finally, the signal samples received for both the presence and absence of the PU are sensed by using RVM. The experimental results show that the proposed algorithm has high classification detection performance in each low signal-to-noise ratio case, and effectively realizes the perception of the PU signal.