发表机构
King’s College London（伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对IRS辅助混合RF/VLC网络，提出基于协作多智能体DRL的联合优化方法，实现技术选择、功率分配和IRS角度优化，提升比例公平性并优于独立网络及现有算法。
AI 中文摘要
混合射频（RF）和可见光通信（VLC）网络已成为第六代（6G）系统中高容量室内无线连接的有前景的解决方案。然而，有限的光学覆盖范围以及用户移动性下VLC链路对视线（LoS）阻塞的脆弱性仍然是基本挑战。在本工作中，部署了一个基于镜面的智能反射面（IRS）来辅助动态室内混合RF/VLC网络，其中每个移动用户通过二元选择决策被专门分配给IRS增强的VLC子网络或RF子网络。随后，制定了一个联合优化问题，通过联合优化RF/VLC技术选择、功率分配以及IRS镜面的滚动和偏航角，以最大化比例公平性。为了实现实时适应性，该问题被重新表述为马尔可夫决策过程（MDP），并使用基于集中训练与分散执行的协作多智能体深度强化学习（DRL）算法求解。仿真结果表明，与优化的独立VLC和RF网络相比，优化的混合网络具有优越性能。结果进一步验证了所提出的DRL框架相对于广泛采用的DRL算法以及传统的基于模型的优化方法的实用性和有效性。
英文摘要
Hybrid radio frequency (RF) and visible light communication (VLC) networks have emerged as a promising solution for high-capacity indoor wireless connectivity in sixth-generation (6G) systems. However, the limited optical coverage and vulnerability of VLC links to line-of-sight (LoS) blockage under user mobility remain fundamental challenges. In this work, a mirror-based intelligent reflecting surface (IRS) is deployed to assist a dynamic indoor hybrid RF/VLC network, where each mobile user is exclusively assigned to either the IRS-enhanced VLC subnetwork or the RF subnetwork through a binary selection decision. A joint optimization problem is then formulated to maximize proportional fairness by jointly optimizing the RF/VLC technology selection, power allocation, and IRS mirror roll and yaw orientation angles. To enable real-time adaptability, the problem is reformulated as a Markov decision process (MDP) and solved using a cooperative multi-agent deep reinforcement learning (DRL) algorithm based on centralized training with decentralized execution. Simulation results demonstrate the superior performance of the optimized hybrid network compared with optimized standalone VLC and RF networks. The results further validate the practicality and effectiveness of the proposed DRL framework compared to widely adopted DRL algorithms as well as conventional model-based optimization approaches.
Comments6 pages, 4 figures
