Multi-user massive MIMO channel estimation using joint sparsity and non-ideal feedback modeling

In order to realize the advantages of the massive MIMO systems, the channel state information must be obtained at the base station (BS). However, it is a challenging task in the frequency division duplexing (FDD) systems due to the overwhelming overhead of downlink channel pilots and uplink channel feedback. In this paper, we consider the multi-user massive MIMO system and apply joint sparsity-based technique to reduce the overhead. Due to the shared scatterers in the physical propagation environment, the user channel matrices have a joint sparsity structure which has been utilized in the proposed algorithm. Furthermore, using the common sparsity pattern of the uplink and downlink channel, we have suggested a mixed weighted smoothed L-0/L-2 norm minimization technique to estimate the downlink channel with the aid of the uplink channel support. Moreover, in most of the works in the literature, the feedback channel has been considered as a simple additive white Gaussian noise (AWGN) channel which may result in poor performance in the real scenarios. To address this issue, we have presented a better modeling of the uplink feedback channel to increase the channel estimation accuracy and decrease the number of required pilot sequences. We have investigated the performance of the proposed method in different simulation scenarios. The results confirm that the suggested scheme is superior to the other state-of-the-art techniques in terms of the recovery accuracy and the pilot and feedback overhead. (C) 2019 Published by Elsevier Inc.