Adaptive Rate and Energy Harvesting Interval Control Based on Reinforcement Learning for SWIPT

In this letter, we propose a new adaptive rate and energy harvesting interval control scheme to maximize the throughout subject to the average energy constraint in the multiple-input single-output simultaneous wireless information and power transfer system. We consider the realistic scenario of time-varying fading channel. In order to maximize the throughput and simultaneously to maintain the average energy required at the receiver, we first formulate a problem of jointly optimizing the rate and energy harvesting interval based on a Markov decision process (MDP) by using a regularization parameter. However, this MDP problem is difficult to directly solve because the channel transition probabilities (i.e., the model or the environment) are challenging to estimate in the practical systems. Thus, we propose an adaptive rate and energy harvesting interval control algorithm based on the model-free reinforcement learning technique. Numerical results demonstrate that the proposed scheme significantly outperforms the conventional scheme.