We devise a partially observable Markov decision process (POMDP) based joint antenna selection and user scheduling (JASUS) policy for a massive MIMO base station, equipped with only a small number of RF chains, that serves a large number of users. The users are served at different time slots within a frame. Relying on partial CSI obtained from training between the selected antennas and the users, at the beginning of each frame, the BS assigns each user to a time slot in the frame and selects a subset of antennas to serve the users scheduled in each time slot. Assuming that the channels evolve according to a Markov process and relying on zero-forcing beamforming, we formulate our JASUS problem using a POMDP framework to devise a real-time decision-making policy that maximizes the expected long-term sum-rate. We rigorously prove that for positively correlated two-state channel models, the myopic policy provides the optimal solution to our POMDP-based JASUS problem for any number of RF chains and for any number of users. Based on this, we model the Rayleigh fading channels as first-order Gauss-Markov processes and devise a low-complexity myopic policy-based JASUS algorithm for massive MU-MIMO systems that only relies on partial CSI.

We use a partially observable Markov decision process (POMDP) framework to design an optimal antenna selection policy for downlink transmit beamforming at a multi-antenna base station (BS) equipped with only a limited number of RF chains. Assuming that the channel state evolves according to a finite-state Markov process and that only the channel coefficients which correspond to previously selected antennas, are available at the BS, we use the POMDP framework for antenna selection with the aim to maximize thelong-term expected downlink data rate. To avoid the high computational complexity of the value iteration algorithm, we focus on the myopic policy and prove thatin the case of positively correlated two-state Markov model for the channel over each antenna, the myopic policy is optimal for antenna selection for any number of RF chains. Based on this finding, for general fading channels, we propose to quantize each channel into two levels and apply the myopic policy for antenna selection. Our simulation results show that using this two-state coarse channel quantization for antenna selection results in only a small loss in performance, as compared to the antenna selection technique which uses full channel state information without quantization.

We use a partially observable Markov decision process (POMDP) framework to formulate the problem of antenna selection for a base-station, equipped with a large-scale antenna array and a smaller number of RF chains. Assuming that the fading channel evolves according to a finite-state Markov chain and that only partial channel state information (CSI) from the limited selected antennas is available at each time slot, we rely on a POMDP framework for antenna selection to maximize the long-term expected downlink data rate. To avoid the computational complexity associated with the value iteration algorithm, we herein propose to use the simple myopic antenna selection policy based on the fact that for any arbitrary number of antennas and RF chains, under the assumption of positively correlated two-state Markov channel model, the myopic policy is optimal. To apply the optimal myopic policy-based antenna selection for general fading channels, we propose to quantize the channels into two values only for the purpose of antenna selection. Interestingly, our results show that the performance of the myopic antenna selection policy is close to that of the policy which relies on un-quantized full CSI.