Reinforcement Learning Based Antenna Beam Selection for Wireless Communications in Urban Environments

Today, machine learning has a crucial role in wireless communications, notably in 5G and 6G. It contributes significantly for increasing network capacity, improving user experience, and enhancing network reliability. Among machine learning techniques, reinforcement learning is vital due to its suitability for many real-world scenarios. It enables agents to learn from the environment with zero-knowledge and make rational decisions. Thus, in this article, we aim to explore the role of classical reinforcement learning in predicting optimal beam angles within urban environments. The goal is to minimize interference between antennas by finding optimal beamforming angles using ray tracing techniques. We examine various classic reinforcement learning methods in an urban scenario, focusing on maximizing total channel capacity. Initially, we identify the optimal beamforming angles for maximizing channel capacity with four antennas. After validating the learning methods and achieving over 99% accuracy, we proceeded to utilize them in a larger scenario. In the first phase, these methods and their accuracy are validated based on the results of the exhaustive search for a small number of nodes. In the second phase, we predict optimal antenna beam angles for scenarios with an increased number of transmitters and receivers for a realistic urban environment situated in the north-eastern part of Berlin.