Energy‐efficient resource allocation over wireless communication systems through deep reinforcement learning

Summary As the popularity of the Internet of Things (IoT) increases, so do the energy requirements of IoT terminal equipment. To address the energy shortage problem of equipment and ensure continuous and stable operation in light of renewable energy and an uncertain environment, a rational and efficient energy allocation strategy is required. This paper proposes a deep reinforcement learning energy allocation strategy that uses the DQN algorithm to directly interact with the unknown environment. The best energy allocation method is independent of environmental knowledge, and a pretraining algorithm is proposed to maximise the initialization state of the strategy. Experiments of comparison and simulation are conducted under various channel data circumstances. Results indicate that the proposed energy allocation strategy outperforms the current strategy in multiple channel conditions and has a high capacity for adaptation to changing conditions. This study introduces a deep reinforcement learning energy allocation strategy for Internet of Things terminal devices. It employs the DQN algorithm to interact with the environment and resolve the energy shortage issue in the presence of renewable energy and unpredictability.