Power Control in 5G Heterogeneous Cells considering User Demands using Deep Reinforcement Learning

Heterogeneous cells have been emerged as the dominant design ap-proach for the deployment of 5G wireless networks. In this context, inter-cell interferences are expected to drastically affect the 5G targets, especially in terms of throughput experienced by the mobile users. This work proposes a novel Deep Reinforcement Learning (DRL) scheme, targeting at minimizing the difference between the allocated and requested user throughput through power regulation. The developed algorithm is employed in heterogeneous cells that are controlled in a centralized manner and validated for 5G-compliant channel models. First, the proposed learning framework of the DRL method is presented, mainly including the stabilization of the learning-related hyperpa-rameters. Then, the DRL method is evaluated for several simulation scenarios and compared to well-established optimization methods for power allocation, namely the Water-filling and Weighted Minimum Mean Squared Error (WMMSE) algorithms, as well as a fixed power control scheme. The evaluation outcomes demonstrate the ability of the DRL framework in accurately ap-proaching the user requirements, whereas the Water-filling and WMMSE solu-tions present large deviations from the user demands since they aim at the total network-wide throughput maximization