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Reinforcement Learning Based Antenna Selection in User-Centric Massive MIMO

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.