Recent trends in 5G and beyond wireless networks have encouraged the migration from the already congested radio frequency (RF) spectrum to higher frequency bands. In this context, the ubiquitous presence of lighting systems supports wide scale deployment of wireless communication links via light. However, the susceptibility of light to user mobility hinders its wide adoption. Hence, the coexistence of RF and light-based wireless communications has the potential to offer seamless heterogeneous network (HetNet) coverage through intelligent vertical handover policies in the presence of users' mobility. In this article, we first present new insights on the implementation of realistic indoor mobile optical channels and the impact of crowd mobility on relevant channel statistics. Then, an artificial intelligence (AI)-based framework for efficient resource management in mobile multi-user RF-light HetNets is proposed using a deep learning-empowered optical link predictor and a multiagent reinforcement learning-based link assignment strategy. The proposed AI-based framework helps to lay down the foundations of smart resource management in mobile multi-user HetNets.

中文翻译：

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用于多用户移动异构射频光网络中智能资源管理的人工智能


5G 及其他无线网络的最新趋势鼓励从已经拥挤的射频 (RF) 频谱迁移到更高的频段。在这种背景下，无处不在的照明系统支持通过光进行无线通信链路的大规模部署。然而，光对用户移动性的敏感性阻碍了其广泛采用。因此，射频和基于光的无线通信的共存有可能在用户移动的情况下通过智能垂直切换策略提供无缝异构网络（HetNet）覆盖。在本文中，我们首先提出了关于现实室内移动光通道的实施以及人群流动对相关通道统计数据的影响的新见解。然后，使用深度学习支持的光链路预测器和基于多智能体强化学习的链路分配策略，提出了一种基于人工智能（AI）的移动多用户射频光异构网络中高效资源管理的框架。所提出的基于人工智能的框架有助于为移动多用户异构网络中的智能资源管理奠定基础。