Channel Estimation for Multi-User Massive MIMO Systems

‍ ‍Obtaining channel state information of massive multiple-input multiple-output （MIMO） systems is crucial. In frequency division duplexing （FDD） mode， the conventional multi-user channel estimation problem is to decompose the multi-user MIMO system into multiple single-user MIMO systems and use the channel characteristics of the single user for estimation and reconstruction. However， with the increase in the number of antennas at the base station and the number of users， not only the pilot overhead and error of the reconstruction algorithm increase gradually， but also the computational complexity increases， resulting in a decline of the overall system performance. To address this problem， this study proposes a multi-user joint channel estimation scheme based on the compressed sensing multiple measurement vector （MMV） model. First， based on the common sparsity and independent sparsity structure among the channels in the angle domain of geographically adjacent users in the multi-user massive MIMO system， a channel sparsity estimation strategy suitable for the MMV model is designed. The channel sparsity estimation strategy is obtained by ranking the contribution rate of sparse components， which improves the performance of the reconstruction algorithm under the condition of unknown or inaccurate sparse prior information. Second， a segmented residual dynamic feedback joint matching pursuit （SRDFMP） algorithm is proposed. The algorithm uses several innovative techniques： support sets with different attributes are differentiated and estimated in segments， effectively reducing the pilot frequency overhead； the common support sets of all users are shared to avoid some redundant iteration steps； and two index length updating criteria are set according to different supports to accelerate the convergence speed of the algorithm. Simultaneously， the problem of correcting the wrong atoms is also considered. Finally， a standardized spatial channel model （SCM） is constructed to validate the performance of the proposed algorithm. Simulation results show that， compared with conventional algorithms， the proposed algorithm has lower pilot cost， better channel estimation performance， and higher joint recovery efficiency under multi-user conditions.