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

• URL: http://arxiv.org/abs/2408.03631v2
• Date: Thu, 26 Dec 2024 02:14:28 GMT
• Title: Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization
• Authors: Yanhu Wang, Muhammad Muzammil Afzal, Zhengyang Li, Jie Zhou, Chenyuan Feng, Shuaishuai Guo, Tony Q. S. Quek,
• Abstract summary: Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering. Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
• Score: 62.16747639440893
• License:
• Abstract: 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.

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