Research and Applications of LLM-Based Assisted Planning Capabilities for Wireless Networks
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Abstract
With the rapid advancement of large language models (LLMs) in natural language processing, these models have demonstrated substantial potential in data processing, pattern recognition, and predictive analytics. This capability offers new perspectives for traditional wireless network base-station planning and design. Building upon current LLM technologies, this work introduces Agents, prompt-engineering methodologies, and chain-of-thought reasoning to enable interactive analysis of fundamental coverage scenarios. By integrating Retrieval-Augmented Generation (RAG), we construct a domain knowledge base tailored to network planning, supporting standardized verification, specification matching, and knowledge retrieval throughout the planning workflow. Furthermore, to ensure the security of sensitive information, we propose a private-domain deployment architecture in which all data are processed exclusively within internal networks and servers, thereby providing robust protection for end-to-end data security.
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