In this paper, we present \textit{CTMAP}, a large language model (LLM) empowered digital twin framework for connectivity-aware route navigation in millimeter-wave (mmWave) wireless networks. Conventional navigation tools optimize only distance, time, or cost, overlooking network connectivity degradation caused by signal blockage in dense urban environments. The proposed framework constructs a digital twin of the physical mmWave network using OpenStreetMap, Blender, and NVIDIA Sionna's ray-tracing engine to simulate realistic received signal strength (RSS) maps. A modified Dijkstra algorithm then generates optimal routes that maximize cumulative RSS, forming the training data for instruction-tuned GPT-4-based reasoning. This integration enables semantic route queries such as ``find the strongest-signal path'' and returns connectivity-optimized paths that are interpretable by users and adaptable to real-time environmental updates. Experimental results demonstrate that CTMAP achieves up to a tenfold improvement in cumulative signal strength compared to shortest-distance baselines, while maintaining high path validity. The synergy of digital twin simulation and LLM reasoning establishes a scalable foundation for intelligent, interpretable, and connectivity-driven navigation, advancing the design of AI-empowered 6G mobility systems.

翻译：本文提出 \textit{CTMAP}，一种基于大语言模型（LLM）的数字孪生框架，用于毫米波（mmWave）无线网络中的连通性感知路径导航。传统导航工具仅优化距离、时间或成本，忽视了密集城市环境中信号遮挡导致的网络连通性下降。该框架利用 OpenStreetMap、Blender 和 NVIDIA Sionna 的光线追踪引擎构建物理毫米波网络的数字孪生，以模拟真实的接收信号强度（RSS）地图。随后，采用改进的 Dijkstra 算法生成最大化累积 RSS 的最优路径，这些路径构成基于指令微调 GPT-4 的推理训练数据。该集成框架支持语义化路径查询（例如“寻找信号最强的路径”），并返回用户可理解且能适应实时环境更新的连通性优化路径。实验结果表明，与最短距离基线相比，CTMAP 在保持高路径有效性的同时，实现了累积信号强度高达十倍的提升。数字孪生仿真与 LLM 推理的协同作用，为智能、可解释且以连通性驱动的导航建立了可扩展的基础，推动了人工智能赋能的 6G 移动系统设计。