Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

2024-10-07

arXiv.org

AI Summary

This paper addresses the joint optimization of window spatial placement and beam steering of window-mounted reconfigurable intelligent surfaces (RIS) in outdoor-to-indoor RIS-aided networks, subject to daylighting constraints quantified by the daylight factor (DF). Method: Departing from conventional numerical optimization paradigms, we propose a novel zero-shot optimizer based on multimodal large language models (MLLMs), unifying architectural configuration design and wireless resource allocation. The framework integrates RIS beamforming, 3D channel modeling, DF-aware daylighting simulation, and structured prompt engineering. Contribution/Results: Our MLLM-driven approach achieves superior initial performance, faster convergence, higher final capacity gain, and lower time complexity compared to classical optimization algorithms. Experimental results demonstrate significant improvement in indoor downlink data rates while guaranteeing 100% compliance with daylighting standards.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Optimize wireless and daylight via RIS window positioning
• Jointly enhance wireless rate and daylight illumination
• LMWO framework outperforms classic optimization methods

Innovation

• Optimizes window and RIS positions using LLM
• Multi-modal LLM framework for zero-shot optimization
• Enhances wireless and daylight performance jointly