Joint CSIT acquisition based on low-rank matrix recovery for FDD massive MIMO systems

Channel state information at the transmitter (CSIT) is essential for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, but conventional solutions involve overwhelming overhead both for downlink channel training and uplink channel feedback. In this paper, we propose a joint CSIT acquisition scheme based on low-rank matrix recovery to reduce the overhead. Particularly, unlike conventional schemes where users individually estimate the channel and then feeds back the estimated CSI to the BS to realize CSIT, we propose that all scheduled users feed back their received pilots directly to the BS without individual channel estimation, and then joint MIMO channel estimation can be realized at the BS. We further formulate the joint channel estimation problem at the BS as a low-rank matrix recovery problem by utilizing the low-rank property of the massive MIMO channel matrix, which is caused by the limited number of clusters. Finally, we propose a hybrid low-rank matrix recovery algorithm based on the singular value projection to solve this problem, which can provide accurate CSIT with low overhead as demonstrated by simulations.