Antenna Selection for Massive MIMO Systems Based on POMDP Framework

Abstract

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.