Online Energy-Efficient Beam Bandwidth Partitioning in mmWave Mobile Networks

This paper studies beam bandwidth partitioning problem in mobile millimeter-wave (mmWave) and multiple antennas networks. The main novelty is to flexibly optimize the beamforming bandwidth with the aim to minimize the energy consumption of the system while guaranteeing the data requirements of all mobile users. We formulate the problem as an integer nonlinear programming problem. To efficiently solve the problem, we design a deep reinforcement learning using the proximal policy optimization approach and train a deep neural network in an on-policy manner. Then, for comparison purposes, we develop low-complexity online iterative accurate solutions. We show that our approach achieves better performance compared to the iterative solutions and is able to achieve at least 4% less energy consumption and more than 12% energy efficiency gains.