Visible light communication (VLC) has been widely applied as a promising solution for modern short range communication. When it comes to the deployment of LED arrays in VLC networks, the emerging ultra-dense network (UDN) technology can be adopted to expand the VLC network's capacity. However, the problem of inter-cell interference (ICI) mitigation and efficient power control in the VLC-based UDN is still a critical challenge. To this end, a reinforcement learning (RL) based VLC UDN architecture is devised in this paper. The deployment of the cells is optimized via spatial reuse to mitigate ICI. An RL-based algorithm is proposed to dynamically optimize the policy of power and interference control, maximizing the system utility in the complicated and dynamic environment. Simulation results demonstrate the superiority of the proposed scheme, it increase the system utility and achievable data rate while reducing the energy consumption and ICI, which outperforms the benchmark scheme.

翻译：可见光通信（VLC）已成为现代短距离通信的一种有前景的解决方案。当在VLC网络中部署LED阵列时，可以采用新兴的超密集网络（UDN）技术来扩展VLC网络的容量。然而，在基于VLC的UDN中，小区间干扰（ICI）抑制和高效功率控制问题仍是一个关键挑战。为此，本文设计了一种基于强化学习（RL）的VLC UDN架构。通过空间复用优化小区部署以抑制ICI。提出了一种基于强化学习的算法，用于动态优化功率与干扰控制策略，在复杂动态环境中最大化系统效用。仿真结果表明，所提方案具有优越性，其在降低能耗和ICI的同时提高了系统效用和可达数据速率，性能优于基准方案。