CTMap: LLM-Enabled Connectivity-Aware Path Planning in Millimeter-Wave Digital Twin Networks
🤖 AI Summary
This work proposes a semantic path planning approach that integrates digital twins with large language models to address the degradation of network connectivity in dense urban environments caused by frequent blockages of millimeter-wave signals. A three-dimensional scene is constructed using OpenStreetMap, and high-fidelity signal strength maps are generated via ray tracing with Blender and NVIDIA Sionna. By combining an enhanced Dijkstra algorithm with an instruction-tuned GPT-4 model, the method enables natural language-driven, connectivity-aware navigation. This study represents the first integration of large language models with millimeter-wave digital twin networks, achieving up to a tenfold improvement in cumulative signal strength over shortest-path baselines while maintaining path validity and supporting interpretable, dynamic-environment navigation.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
mmWave networks
connectivity-aware navigation
signal blockage
digital twin
path planning
Innovation
Methods, ideas, or system contributions that make the work stand out.
LLM-enabled navigation
connectivity-aware path planning
mmWave digital twin
ray-tracing simulation
semantic route query
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