Space-time correlation based fast regional spectrum sensing in cognitive radio

In this paper, a space-time correlation based fast regional spectrum sensing (RSS) scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes, and all meshes are clustered into highly related groups using the spatial correlation among them. In each group, some representative meshes are selected as detecting meshes (DMs) using a multi-center mesh (MCM) clustering algorithm, while other meshes (EMs) are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus, detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results, minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing, our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.