CTMap: LLM-Enabled Connectivity-Aware Path Planning in Millimeter-Wave Digital Twin Networks
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.