Distributed Compressed Sensing of Doubly Selective Channel in Massive MIMO Systems

With the number of antennas and users increases significantly, and the complexity of channel estimation and pilot overhead increase continuously in Massive MIMO systems. To solve this problem, in this paper, a doubly selective channel estimation method based on distributed compressed sensing (DCS) is proposed for large-scale MIMO system. Firstly, the problem of doubly selective channel estimation is formulated under the framework of DCS. Then the joint sparsity of doubly selective channel is theoretically proved by showing that the channel coefficients in transformed domain have the spatial correlation between the adjacent antennas. By exploiting the joint sparsity of transformed domain coefficients corresponding to the channel between different transmit and receive antennas, the channel coefficients is reconstructed by distributed compressed reconstruction algorithm. The simulation results show that the proposed approach can reconstruct the original channel coefficient effectively with significantly reduced pilot overhead.