Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial

2025-09-07

AI Summary

Large-scale combinatorial optimization problems—such as resource allocation and trajectory design in 6G wireless networks—face critical challenges in dynamic heterogeneous environments, including poor real-time responsiveness, limited scalability, and insufficient user intent understanding. Method: This paper proposes a large language model (LLM)-driven framework for semantic understanding and structured reasoning. It integrates natural language modeling, solver-augmented co-reasoning, and solution verification, jointly leveraging deep reinforcement learning and semantic inference to enable end-to-end mapping from user intent to optimization models. Contribution/Results: Compared with conventional heuristic and deep reinforcement learning approaches, the framework achieves significant improvements in inference latency and cross-scenario generalization. It natively supports emerging paradigms such as low-altitude economy networking and intent-driven networking. Furthermore, the study systematically surveys LLM application architectures, representative use cases, open-source toolkits, and benchmark datasets in wireless networks—providing both theoretical foundations and practical guidelines for building trustworthy, scalable intelligent network optimization systems for next-generation wireless infrastructure.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), struggle to meet the demanding requirements of real-time adaptability, scalability, and dynamic handling of user intents in increasingly heterogeneous and resource-constrained network environments. Large language models (LLMs) present a transformative paradigm by enabling natural language-driven problem formulation, context-aware reasoning, and adaptive solution refinement through advanced semantic understanding and structured reasoning capabilities. This paper provides a systematic and comprehensive survey of LLM-enabled optimization frameworks tailored for wireless networks. We first introduce foundational design concepts and distinguish LLM-enabled methods from conventional optimization paradigms. Subsequently, we critically analyze key enabling methodologies, including natural language modeling, solver collaboration, and solution verification processes. Moreover, we explore representative case studies to demonstrate LLMs' transformative potential in practical scenarios such as optimization formulation, low-altitude economy networking, and intent networking. Finally, we discuss current research challenges, examine prominent open-source frameworks and datasets, and identify promising future directions to facilitate robust, scalable, and trustworthy LLM-enabled optimization solutions for next-generation wireless networks.

Problem

• Addressing complexity in 6G wireless network optimization problems
• Overcoming limitations of traditional optimization methods like DRL
• Leveraging LLMs for adaptive, scalable network management solutions

Innovation

• LLMs enable natural language-driven problem formulation
• LLMs provide context-aware reasoning for wireless networks
• LLMs support adaptive solution refinement through semantic understanding