用大模型优化窗户与智能表面位置，兼顾信号与采光。
Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model
- 用多模态大模型零样本优化窗户和智能表面位置。
- 相比传统方法，收敛快、性能优，无线速率显著提升。
- 适合建筑智能化设计与无线网络规划人员参考。
本文旨在通过调整窗户位置及部署于窗户上的可重构智能表面（RIS）的波束方向，同时优化室内无线性能与自然采光效果，针对基于大语言模型（LLM）的智能优化器，构建了面向RIS辅助室外到室内（O2I）网络的联合优化框架。首先，建立无线与采光系统模型，并提出联合优化问题以提升无线总吞吐量与光照性能。随后，设计一种基于多模态大语言模型的窗口优化（LMWO）框架，结合提示模板实现零样本优化，兼具建筑师与无线网络规划者双重角色。最后，分析了窗户数量、房间尺寸、RIS单元数与采光因子对优化效果的影响。数值结果表明，相较于经典优化方法，所提LMWO框架在初始性能、收敛速度、最终结果与时间复杂度方面均表现优异，显著增强建筑无线性能的同时保障室内采光质量。
原文摘要 · Abstract (English)
This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building's wireless performance can be significantly enhanced while ensuring indoor daylight performance.
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