  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. This integration represents the future paradigm of artificial
intelligence (AI) as a service and AI for more ease. As a preliminary
exploration, this research first develops a novel LLM-empowered BSS
optimization framework, and heuristically proposes four different potential
implementations: the strategies based on Prompt-optimized LLM (PoL),
human-in-the-Loop LLM (HiLL), 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, cost-effective, and
reliable network deployments, noticeably enhancing the efficiency of BSS
optimization and reducing trivial manual participation.
