Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications

Abstract

To overcome the difficulties of charging the wireless sensors in the wild with conventional energy supply, more and more researchers have focused on the sensor networks with renewable generations. Considering the uncertainty of the renewable generations, an effective energy management strategy is necessary for the sensors. In this paper, we propose a novel energy management algorithm based on the reinforcement learning. By utilizing deep deterministic policy gradient (DDPG), the proposed algorithm is applicable for the continuous states and realizes the continuous energy management. We also propose a state normalization algorithm to help the neural network initialize and learn. With only one day's real solar data and the simulative channel data for training, the proposed algorithm shows excellent performance in the validation with about 800 days length of real solar data. Compared with the state-of-the-art algorithms, the proposed algorithm achieves better performance in terms of long-term average net bit rate.