面向无蜂窝 OIRS 辅助 OWC 网络的部署、朝向与资源分配优化
Placement, Orientation, and Resource Allocation Optimization for Cell-Free OIRS-aided OWC Network
AI总结：
针对无蜂窝 OWC 网络，本文联合优化发射功率、接收机朝向、OIRS 单元分配与部署，基于 ε-约束和连续凸近似求解兼顾 SE 与 EE 的多目标问题。
摘要：
光学智能反射面（OIRS）技术的出现标志着光学无线通信（OWC）系统的一个里程碑，使人们能够增强对室内环境中光传播的控制。这一能力允许对信道条件进行定制，以实现特定性能目标。本文通过融合 OIRS，对下行无蜂窝 OWC 网络进行增强。核心重点在于精细调整关键参数，包括发射功率、接收机朝向、OIRS 单元分配以及策略性部署。具体而言，文中构建了一个多目标优化问题（MOOP），旨在同时提升网络的频谱效率（SE）和能量效率（EE），同时满足网络的服务质量（QoS）约束。该问题通过采用 ε-约束方法求解，将 MOOP 转化为单目标优化问题，并借助连续凸近似进行求解。仿真结果表明 OIRS 对 SE 和 EE 具有显著影响，证实了其在改善 OWC 网络性能方面的有效性。
英文摘要：
The emergence of optical intelligent reflecting surface (OIRS) technologies marks a milestone in optical wireless communication (OWC) systems, enabling enhanced control over light propagation in indoor environments. This capability allows for the customization of channel conditions to achieve specific performance goals. This paper presents an enhancement in downlink cell-free OWC networks through the integration of OIRS. The key focus is on fine-tuning crucial parameters, including transmit power, receiver orientations, OIRS elements allocation, and strategic placement. In particular, a multi-objective optimization problem (MOOP) aimed at simultaneously improving the network's spectral efficiency (SE) and energy efficiency (EE) while adhering to the network's quality of service (QoS) constraints is formulated. The problem is solved by employing the $ε$-constraint method to convert the MOOP into a single-objective optimization problem and solving it through successive convex approximation. Simulation results show the significant impact of OIRS on SE and EE, confirming its effectiveness in improving OWC network performance.
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。
