A Survey on Applications of Large Language Model-Driven Digital Twins for Intelligent Network Optimization

With the widespread application of digital twin (DT) technology in network optimization and intelligent management, its integration with large language models (LLMs) presents immense potential. LLMs excel in natural language processing, multimodal analysis, and real-time optimization, enabling innovative solutions for intelligent monitoring, resource allocation, and decision-making in complex network environments. This paper systematically reviews the development of DTs and LLMs, elaborates on their core principles and application scenarios, and examines the capabilities of LLM-driven DTs in key network optimization tasks, including traffic prediction, fault diagnosis, resource allocation, and multi-objective optimization. By leveraging real-time data from DTs, LLMs can dynamically generate optimization strategies, enabling precise monitoring and intelligent tuning. Furthermore, this paper explores the potential of integrating LLMs and DTs to address complex challenges such as data quality, latency sensitivity, and energy consumption demands, while summarizing existing technical bottlenecks. Finally, the paper proposes several potential research directions to address these challenges, offering a comprehensive perspective for advancing the efficiency and automation of next-generation intelligent networks.