Distributed Reinforcement Learning Based Adaptive Antenna Pattern Selection for Wireless Communication Networks

Fifth generation (5G) New Radio (NR), was designed to offer more flexibility to satisfy new service requirements. However, this implies the increasing complexity of maintaining and configuring cellular network. Meanwhile, deep learning has proven successful in a variety of aspects, such as natural language processing and computer vision, in particular, which is proven to have a performance that is proportional to the total amount of training data and with faster inference time than traditional optimization algorithm. In NR, beam management is a procedure that chooses the beams to combat path loss attenuation according to signal strength, which is more critical in future millimeter-wave (mmWave) communication system. However, in the mobile communication, the misaligned beam issue is one of the serious problems to be solved. The beam aligns between transmitter and receiver does not match will cause signal-to-interference-plus-noise ratio (SINR) degradation which will directly influence the users’ experience. In this paper, we present an adaptive antenna pattern management scheme based on distributed reinforcement learning (RL) to solve misaligned beam management problem. More specifically, using distributed RL algorithm, the agent learns over time the best combination of antenna patterns among base stations (BSs) to maximize the overall SINR.