Reinforcement Learning-Based Global Programming for Energy Efficiency in Multi-Cell Interference Networks

With the increasing application of internet of things (IoT), the number of wirelessly transmitting devices is on a rise. It is important that the energy efficiency (EE) is maximized to reduce interference and save energy. This work explores the possibility of power control for maximum EE in wireless interference networks using reinforcement learning (RL) techniques. We apply the soft actor-critic (SAC) algorithm based on entropy regularization that allows to escape local optima and foster exploration. This enables us to solve the energy efficient power control problem with reduced complexity. We demonstrate that the obtained solutions are close to the global optimum. In contrast to supervised machine learning (ML) techniques, we do not need any kind of labeled data in the training phase. The model free approach and the unsupervised nature of RL therefore reduce the required computational effort and has a better scalability as a consequence.