Real-Time 3-D MIMO Antenna Tuning With Deep Reinforcement Learning

Abstract

The 3D MIMO in 5G system requires adaptive and real-time adjustment of antenna azimuth angle, downtilt and beam combination according to the user distribution, which is a powerful technique to improve coverage and capacity. However, due to the complicated interactions between cells and complex environments, it is challengeable to jointly tune the antenna of multiple cells. Besides, the user distribution is complicated because of huge number of users and variable user locations. In this paper, a practical real-time 3D MIMO antenna tuning method with deep reinforcement learning (DRL) is proposed to jointly adjust the antenna configuration according to time-varying user distributions to improve the coverage and access performance of the system. Specifically, a deep neural network is trained to configure the antenna parameters and the Multi-agent reinforcement learning (MARL) is used to adapt various user distributions. The proposed method has been verified on a 5G simulation environment with real geographical features from the real network of China Mobile. Simulation results indicate that the proposed method can improve the coverage and access performance compared with the typical schemes used in practical networks.