A Joint Mechanism of Adaptive Bayesian Compressed Channel Sensing Based on Optimized Measurement Matrix

In this paper, a common process of compressed channel estimation is given by the Bayesian Compressed Sensing (BCS) which is based on the probability principle of Maximum A Posteriori (MAP). In the process, signal reconstruction and measurement matrix design as two separate steps can be combined together by Adaptive BCS (ABCS). Meanwhile a joint mechanism of ABCS and optimized measurement is proposed by reducing the coherence and the adaptive design of measurement matrix to get a better reconstruction performance. Furthermore, the error bars obtained in the process of reconstruction can be used to measure the accuracy of the reconstruction. Simulation results show that under the same conditions, the joint mechanism shows better anti-noise ability and recovery accuracy than those of the traditional reconstruction algorithm.