综述大模型在通信网络管理中的应用，涵盖多场景挑战与未来方向。
A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions
- 构建跨移动、车联网、云边网络的LLM应用分类体系
- 系统梳理大模型在监控、规划、部署与支持中的实际作用
- 适合关注智能网络管理的科研人员与工程实践者
近年来通信网络的快速发展，对高效、可扩展、高性能且可靠的网络与服务管理（NSM）策略提出了更高要求。大型语言模型（LLMs）因其在自然语言处理任务中的卓越表现和生成上下文感知洞察的能力，展现出自动化通信NSM任务的巨大潜力。与仅聚焦单一网络领域的现有综述不同，本文从移动网络、车联网、基于云的网络以及边缘/雾计算网络等多个通信网络领域出发，系统考察了LLM的集成应用。首先，提供LLM的基础知识，包括通用Transformer架构、通用与领域专用模型、预训练与微调方法及其与通信NSM的关系。随后，基于新型网络监控与报告、人工智能驱动的网络规划、网络部署与分发、持续网络支持四类任务，对各网络域中的LLM应用进行详尽分类，梳理现有文献及其贡献。进而识别当前面临的挑战与开放问题，并提出未来研究方向，强调需发展可扩展、可适应、资源高效的解决方案以应对通信网络动态变化的现实需求。本综述旨在为提升网络服务管理提供全景式路线图。
原文摘要 · Abstract (English)
The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM.
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