Millimeter Wave Communications With an Intelligent Reflector
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 precoding transmission of the BS and the reflection coefficient of the IR are jointly optimized via an iterative approach so as to maximize the sum of downlink rates towards multiple users. 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 approach yields over 30% and 2-fold increase in the downlink sum-rate compared with a fixed IR reflection scheme and direct transmission scheme respectively. Simulation results also show that by deploying more IR elements the downlink sum-rate can be significantly improved. However as the number of IR components increases more time is required for channel estimation and the slope of increase in the IR-aided transmission rate will become smaller. 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.