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

This paper 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 intermittent channels posed by mobility, reinforcer agent training is endowed with tradeoff between energy efficiency communication quality. Accordingly, two typical RL categories, i.e., value-based tabular policy gradient-based deep are imposed compared through several numerical examinations regarding information-power balance, generalization ability, complexity. The vanilla demonstrated to outperform should be prioritized practice if feasible.