# Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization

arXiv: https://arxiv.org/abs/2408.03631 · HTML: https://mlsanity.com/paper/2408.03631/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions](https://mlsanity.com/paper/2308.09936/) (2023)
- [Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving](https://mlsanity.com/paper/2405.12205/) (2024)
- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Learning to Generate Better Than Your LLM](https://mlsanity.com/paper/2306.11816/) (2023)
- [TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models](https://mlsanity.com/paper/2407.09424/) (2024)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions](https://mlsanity.com/paper/1909.11315/) (2019)
- [Large Language Model (LLM)-enabled In-context Learning for Wireless Network Optimization: A Case Study of Power Control](https://mlsanity.com/paper/2408.00214/) (2024)
- [Semantic Importance-Aware Communications Using Pre-trained Language Models](https://mlsanity.com/paper/2302.07142/) (2023)
- [Resource Allocation in Large Language Model Integrated 6G Vehicular Networks](https://mlsanity.com/paper/2403.19016/) (2024)
- [Large Language Model-Driven Curriculum Design for Mobile Networks](https://mlsanity.com/paper/2405.18039/) (2024)
- [Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach](https://mlsanity.com/paper/2209.09828/) (2022)
- [Base-Station Assisted Device-to-Device Communications for High-Throughput Wireless Video Networks](https://mlsanity.com/paper/1304.7429/) (2013)
- [Multi-Path Cooperative Communications Networks for Augmented and Virtual Reality Transmission](https://mlsanity.com/paper/1710.11486/) (2017)
- [Look-Ahead Task Offloading for Multi-User Mobile Augmented Reality in Edge-Cloud Computing](https://mlsanity.com/paper/2305.19558/) (2023)

## Built on by (most cited first, 3 of 3)

- [LLM-hRIC: LLM-empowered Hierarchical RAN Intelligent Control for O-RAN](https://mlsanity.com/paper/2504.18062/) (2025)
- [Planning with Language and Generative Models: Toward General Reward-Guided Wireless Network Design](https://mlsanity.com/paper/2602.00357/) (2026)
- [Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges](https://mlsanity.com/paper/2605.02592/) (2026)

## Abstract (arXiv)

Traditional base station siting (BSS) methods rely heavily on drive testing and user feedback, which are laborious and require extensive expertise in communication, networking, and optimization. As large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering, network optimization will witness a revolutionary approach. This approach entails the strategic use of well-crafted prompts to infuse human experience and knowledge into these sophisticated LLMs, and the deployment of autonomous agents as a communication bridge to seamlessly connect the machine language based LLMs with human users using natural language. Furthermore, our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions, thereby enabling the customization and optimization of the BSS process. This integration represents the future paradigm of artificial intelligence (AI) as a service and AI for more ease. This research first develops a novel LLM-empowered BSS optimization framework, and heuristically proposes three different potential implementations: the strategies based on Prompt-optimized LLM (PoL), LLM-empowered autonomous BSS agent (LaBa), and Cooperative multiple LLM-based autonomous BSS agents (CLaBa). Through evaluation on real-world data, the experiments demonstrate that prompt-assisted LLMs and LLM-based agents can generate more efficient and reliable network deployments, noticeably enhancing the efficiency of BSS optimization and reducing trivial manual participation.