Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning
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In this paper, a novel framework is proposed to optimize the downlink multi-user communication of a millimeter wave base station, which is assisted by a reconfigurable intelligent reflector (IR). In particular, a channel estimation approach is developed to measure the channel state information (CSI) in real-time. First, for a perfect CSI scenario, the optimal precoding transmission and power allocation is derived so as to maximize the sum of downlink rates towards multiple users, followed by the optimization of IR reflection coefficient to enhance the upper bound of the downlink transmission. Next, in the imperfect CSI scenario, a distributional reinforcement learning (DRL) approach is proposed to learn the optimal IR reflection and maximize the expectation of downlink capacity. In order to model the transmission rate's probability distribution, a learning algorithm, based on quantile regression (QR), is developed, and the proposed QR-DRL method is proved to converge to a stable distribution of downlink transmission rate. Simulation results show that, in the error-free CSI scenario, the proposed transmission approach yields over 20 reflection scheme and direct transmission scheme, respectively. Simulation results also show that by increasing the number of IR components, the downlink rate can be improved faster than by increasing the number of antennas at the BS. Furthermore, under limited knowledge of CSI, simulation results show that the proposed QR-DRL method, which learns a full distribution of the downlink rate, yields a better prediction accuracy and improves the downlink rate by 10 for online deployments, compared with a Q-learning baseline.
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