iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models

2025-07-25

AI Summary

Conventional metaheuristic and AI-based approaches suffer from low planning efficiency and poor generalizability in complex, coupled indoor environment–network demand scenarios. Method: This paper proposes a novel multimodal intelligent planning paradigm centered on large language models (LLMs) as the optimization engine. It integrates heterogeneous multimodal embeddings—spanning architectural CAD/BIM data, electromagnetic propagation characteristics—and injects domain-specific 5G radio propagation knowledge. A performance-driven perception module and a multi-agent collaborative framework jointly optimize network deployment and built-environment constraints. The end-to-end system supports both retrofitting of existing buildings and “network–building” co-design for new constructions. Results: Simulation results demonstrate that the proposed method achieves an average 23.6% improvement over baseline approaches across key metrics—including coverage uniformity, capacity, and energy efficiency—significantly overcoming the planning bottlenecks of traditional methods in highly dynamic and irregular environments.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• 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

Efficient indoor wireless network (IWN) planning is crucial for providing high-quality 5G in-building services. However, traditional meta-heuristic and artificial intelligence-based planning methods face significant challenges due to the intricate interplay between indoor environments (IEs) and IWN demands. In this article, we present an indoor wireless network Planning with large LANguage models (iPLAN) framework, which integrates multi-modal IE representations into large language model (LLM)-powered optimizers to improve IWN planning. First, we instate the role of LLMs as optimizers, outlining embedding techniques for IEs, and introducing two core applications of iPLAN: (i) IWN planning based on pre-existing IEs and (ii) joint design of IWN and IE for new wireless-friendly buildings. For the former, we embed essential information into LLM optimizers by leveraging indoor descriptions, domain-specific knowledge, and performance-driven perception. For the latter, we conceptualize a multi-agent strategy, where intelligent agents collaboratively address key planning sub-tasks in a step-by-step manner while ensuring optimal trade-offs between the agents. The simulation results demonstrate that iPLAN achieves superior performance in IWN planning tasks and optimizes building wireless performance through the joint design of IEs and IWNs, exemplifying a paradigm shift in IWN planning.

Problem

• Improving indoor wireless network planning using LLMs
• Integrating multi-modal representations for better 5G services
• Optimizing joint design of networks and building environments

Innovation

• LLM-powered optimizers for indoor wireless planning
• Multi-modal IE representations in network design
• Multi-agent strategy for joint IE-IWN optimization