iPLAN: Redefining Indoor Wireless Network Planning Through Large Language Models

TL;DR

iPLAN leverages large language models with multi-modal indoor environment representations to revolutionize indoor wireless network planning, enabling optimized design and joint environment-network planning for enhanced 5G in-building services.

Contribution

This paper introduces a novel LLM-based framework that integrates indoor environment data into wireless network planning, including joint environment-network optimization and multi-agent collaborative strategies.

Findings

iPLAN outperforms traditional methods in planning accuracy

Joint design of IEs and IWNs improves wireless performance

Multi-agent approach effectively handles complex planning tasks

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.