强化学习用于IRS辅助OWC网络中的速率最大化
Reinforcement Learning for Rate Maximization in IRS-aided OWC Networks
AI总结：
针对室内OWC遮挡导致的中断问题，提出基于Q-learning和SARSA的AP与IRS元件联合分配方法，以低复杂度实现近MILP性能并最高提升45%速率。
摘要：
光无线通信（OWC）被设想为6G网络的主要使能技术之一，可补充射频（RF）系统以提供高数据速率。室内OWC的关键问题之一是由遮挡导致的服务中断，这些遮挡会阻碍用户与其接入点（AP）之间的视距（LoS）链路。最近，被称为智能反射面（IRS）的反射表面被认为可通过将AP信号反射向用户来改善OWC系统的连接性。在本研究中，我们研究将IRS集成到室内OWC系统中，以提高用户的和速率并确保服务连续性。我们构建了一个和速率最大化优化问题，其中同时确定AP和IRS镜面元件对用户的分配，以提升聚合数据速率。此外，提出强化学习（RL）算法，具体为Q-learning和SARSA算法，以在无需先验系统知识的情况下提供低复杂度的实时解决方案。结果表明，RL算法实现了接近混合整数线性规划（MILP）解的近最优解。结果还表明，与仅优化AP分配、而镜面元件根据距离分配给用户的传统方案相比，所提方案实现了最高45%的数据速率提升。
英文摘要：
Optical wireless communication (OWC) is envisioned as one of the main enabling technologies of 6G networks, complementing radio frequency (RF) systems to provide high data rates. One of the crucial issues in indoor OWC is service interruptions due to blockages that obstruct the line of sight (LoS) between users and their access points (APs). Recently, reflecting surfaces referred to as intelligent reflecting surfaces (IRSs) have been considered to provide improved connectivity in OWC systems by reflecting AP signals toward users. In this study, we investigate the integration of IRSs into an indoor OWC system to improve the sum rate of the users and to ensure service continuity. We formulate an optimization problem for sum rate maximization, where the allocation of both APs and mirror elements of IRSs to users is determined to enhance the aggregate data rate. Moreover, reinforcement learning (RL) algorithms, specifically Q-learning and SARSA algorithms, are proposed to provide real-time solutions with low complexity and without prior system knowledge. The results show that the RL algorithms achieve near-optimal solutions that are close to the solutions of mixed integer linear programming (MILP). The results also show that the proposed scheme achieves up to a 45% increase in data rate compared to a traditional scheme that optimizes only the allocation of APs while the mirror elements are assigned to users based on the distance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
