Differential CSIT Acquisition Based on Compressive Sensing for FDD Massive MIMO Systems

To fully exploit advantages of massive MIMO, channel state information at the transmitter (CSIT) is essential to obtain the system performance gains. By far, both channel estimation and channel feedback have been proposed for FDD massive MIMO by exploiting the sparsity of CSI, but they are usually separately discussed, which may impair the CSIT acquisition performance and lead to unnecessary complex computation for users. In this paper, we propose the structured-CS based differential CSIT acquisition scheme for massive MIMO systems, where the downlink channel training and uplink channel feedback are jointly considered. Specifically, we first exploit the temporal correlation of time- varying channels to propose the differential CSIT acquisition scheme, which can reduce both the overhead for downlink training and uplink feedback. Then, we propose the structured compressive sampling matching pursuit (S-CoSaMP) algorithm to further reduce overhead by leveraging the structured sparsity of wireless MIMO channels. Moreover, the proposed differential operation and S-CoSaMP can also be used at users for better channel estimation performance if channel state information at the receiver is needed. Simulation results have demonstrated that the proposed scheme can achieve better CSIT acquisition performance than its counterparts.