This letter investigates hybrid networks composed of a radio frequency (RF) access point (AP) and multiple visible light communication (VLC) APs. We consider mobile multi-homing users that can aggregate resources from both RF and VLC APs. In hybrid RF/VLC networks, RF channel gains vary faster than VLC channels due to small scale fading. By leveraging multi-agent Q-learning to interact with the dynamics of wireless environments, we develop an online two-timescale power allocation strategy that optimizes the transmit powers at the RF and VLC APs to ensure quality-of-service satisfaction. Simulation results demonstrate the effectiveness of the proposed Q-learning based strategy.

中文翻译：

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基于 Q-Learning 的多归属混合 RF/VLC 网络的两时间尺度功率分配

这封信调查了由射频 (RF) 接入点 (AP) 和多个可见光通信 (VLC) AP 组成的混合网络。我们考虑可以从 RF 和 VLC AP 聚合资源的移动多归属用户。在混合 RF/VLC 网络中，由于小规模衰落，RF 信道增益变化比 VLC 信道更快。通过利用多智能体 Q 学习与无线环境的动态交互，我们开发了一种在线双时间尺度功率分配策略，该策略优化了 RF 和 VLC AP 的发射功率，以确保服务质量满意度。仿真结果证明了所提出的基于 Q 学习的策略的有效性。