Massive MIMO downlink channel estimation based on improved CAMP-MMV algorithm

Downlink channel estimation in massive multiple-input multiple-output (MIMO) systems is challenging due to the large training and feedback overhead. So, it is necessary to reduce the pilot overhead. we propose a new compressive sensing (CS)CSI estimation scheme for frequency division duplexing (FDD)massive MIMO systems, which combines the algorithm of supports identify and the complex approximate message passing-multiple measurement vector (CAMP-MMV) algorithm. The approach by using information of supports position to improve the performance of CAMP-MMV. The analytic performance guarantees of the proposed scheme are the length of non orthogonal pilot and signal noise ratio (SNR). The numerical results show that performance of CSI estimation and achieve higher estimation accuracy as compared to an existing sparse Bayesian algorithm.