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Throughput Maximization for Wireless Powered Communication: Reinforcement Learning Approaches

To maximize the throughput of wireless powered communication (WPC), it is critical for the device to decide when to harvest energy, when to transmit data and what transmit power to use. In this paper, we consider a WPC system with a single device using harvest-store-transmit protocol and aim to maximize the longterm average throughput with optimal allocation of the energy harvesting time, data transfer time and the device’s transmit power. With the consideration of many practical constraints including finite battery capacity, time-varying channels and non-linear energy harvesting model, we propose both deep Q-learning (DQL) and actor-critic (AC) approaches to solve the problem and obtain fully online policies. Simulation results show that the performance of our proposed AC approach comes close to that achieved by value iteration and is superior to DQL and other baseline algorithm. Meanwhile, its space complexity is 2-3 orders of magnitude less than that required by value iteration.