Power control in energy harvesting multiple access system with reinforcement learning

Energy harvesting (EH) technique has attracted great attention in Internet of things (IoT) system as it may significantly increase the network lifetime by using renewable energy sources. In this paper, we consider a simple uplink system composed of one base station (BS) and multiple EH user equipments (UEs), where the system control is modeled as a Markov decision process without any prior knowledge assumed on the energy dynamics. The central controller is the BS, which is in charge of scheduling a subset of UEs to access the limited orthogonal channels and regulating transmission power for the scheduled UEs. We propose an actor-critic deep Q-network based (DQN) reinforcement learning (RL) algorithm to handle such a technically challenging problem with continuous state and action spaces. Experiment results show that the proposed RL algorithm can achieve better performances compared with the existing benchmarks.