A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

TL;DR

The paper surveys the use of large language models (LLMs) for network and service management (NSM) across mobile, vehicular, cloud, and fog/edge networks, introducing a transformer-based foundation and a four-task taxonomy: monitoring, AI-powered planning, deployment/distribution, and continuous support. It systematically reviews across four domains, highlighting representative LLM-enabled approaches (e.g., NWDAF-focused Mobile-LLaMA, TelecomGPT, IoV-IDS, WirelessLLM) and detailing how domain-specific data and prompts translate high-level intents into actionable network actions. The work identifies major challenges—data scarcity, domain adaptation, real-time inference, energy efficiency, and security/privacy—and outlines directions toward cross-domain adaptive architectures, efficient inference, standardized benchmarks, and robust security. By organizing existing findings into a cohesive NSM-focused framework, the survey provides a practical roadmap for researchers and engineers to leverage LLMs for automated, scalable, and trustworthy network management. Overall, the paper emphasizes the potential of LLMs to automate and unify diverse NSM tasks across heterogeneous networks while acknowledging substantial practical hurdles to be addressed for real-world deployment.

Abstract

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.

Figures (22)

• Figure 1: LLM application in communication network domains.
• Figure 2: PRISMA flow diagram.
• Figure 3: Outline of survey.
• Figure 5: Proposed taxonomy of LLMs for communication NSM.
• Figure 6: LLM for mobile networks and technologies-based NSM.