LAUSR

Antenna selection is a promising technology to achieve a good balance between high transmission capacity and low hardware complexity for massive multiple-input multiple-output (MIMO) systems. However, the design of a near-optimal antenna selection algorithm with low searching complexity is still a challenge. In this paper, we describe a self-supervised learning based Monte Carlo Tree Search (MCTS) method to solve the antenna selection problem for a massive MIMO system. The search process for selecting antennas with maximal channel capacity is converted to a decision-making based problem. Based on the system model of antenna selection, we map the components of a MIMO system to the basic elements of MCTS such as action, tree state, and reward. To improve the search efficiency of the MCTS, we employ a linear regression module to extract the channel features from the channel state information (CSI) and output the prediction to MCTS as prior probability. Since the data and label are generated by the MCTS process itself, the entire process can be considered as a self-supervised learning process. According to the simulation results, the proposed self-supervised learning MCTS-based antenna search method exhibits a high searching efficiency with near-optimal performance, which archives 40% and 15% outage capacity compared with random selection and greedy search selection, respectively. The bit error rate (BER) performance of the proposed method has about 1-dB gain compared to the greedy search selection method.