Reinforcement Learning Based Multi-Access Control with Energy Harvesting

In this paper, we study an uplink wireless system including N energy harvesting (EH) user equipments (UEs) and one base station (BS) with limited access channels. Each UE has a rechargeable battery with finite capacity. The system control is modeled as a Markov decision process without complete prior knowledge assumed at the BS, which also deals with large sizes in both state and action spaces. To handle such an access control problem, we propose a scheduling algorithm that maximizes the expected sum discounted uplink transmission rate based on reinforcement learning (RL) with deep Q-network (DQN) enhancement. Different from the traditional access control solutions that usually assume strong model knowledges, our goal is to achieve a more stable and balanced transmission over a long time horizon in a data-driven fashion. Finally, experiment results show that the proposed RL algorithm can achieve better performances compared with existing benchmarks.