Optimizing Simultaneous Lightwave Information and Power Transfer Under Practical Indoor Mobility With Reinforcement Learning

Abstract

This article investigates reinforcement learning (RL)-based solutions for optimizing resource allocations in simultaneous lightwave information and power transfer (SLIPT) under practical indoor mobility. Encountering the challenges of excessive outages and intermittent channels posed by practical mobility, the reinforcer for agent training is endowed with the tradeoff between energy efficiency and communication quality. Accordingly, two typical RL categories, i.e., value-based tabular RL and policy gradient-based deep RL are imposed and compared through several numerical examinations regarding information-power transfer balance, generalization ability, and complexity. The vanilla tabular RL is demonstrated to outperform the gradient-based deep RL and should be prioritized in practice if feasible.

Keywords

• Deep deterministic policy gradient (DDPG)
• energy harvesting
• $Q$ -learning%22%2C%22default_operator%22%3A%22AND%22%7D%7D%7D"> <named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> </named-content>-learning
• reinforcement learning (RL)
• simultaneous lightwave information and power transfer (SLIPT)
• simultaneous wireless information and power transfer (SWIPT)