Cooperative Spectrum Sensing and Locationing: A Sparse Bayesian Learning Approach

Abstract

Based on the concept of sparse Bayesian learning, an expectation and maximization algorithm is proposed for cooperative spectrum sensing and locationing of the primary transmitters in cognitive radio systems. Different from typical approaches, not only the signal strength, but also the number and the radio power profiles of the primary transmitters are estimated, which greatly facilitates resource management in cognitive radio. Furthermore, the proposed algorithm can still roughly reconstruct the power propagation map of the primary transmitters even when the measurement rate is below the lower bound for which compressive sensing (CS) can reconstruct signals with the l(1)-norm optimization method. Compared with the typical CS and Bayesian CS algorithms, simulation results show that average mean squared errors (MSE) of the estimated power propagation map are lower with the proposed algorithm. Besides, the computational complexity is also lower owing to bases pruning. The MSE of the location estimation are also shown to demonstrate the capability of the proposed algorithm.