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Large Language Models meet Network Slicing Management and Orchestration

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arxiv 2403.13721 v1 pith:GAR3I7ZM submitted 2024-03-20 cs.NI cs.AI

classification cs.NIcs.AI
keywords networkframeworkmanagementorchestrationslicingfutureinfrastructurelanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives

    cs.NI 2025-09 conditional novelty 4.0 of 10

    A survey that organizes LLM and AI reasoning methods into a taxonomy and maps them onto the physical, link, network, transport, and application layers of wireless networks.

  2. Concept-Level AI for Telecom: Moving Beyond Large Language Models

    cs.NI 2025-06 reject novelty 4.0 of 10

    A position paper proposing Large Concept Models as the successor to LLMs for telecom network management, without experimental evidence.

  3. From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications

    cs.AI 2025-05 conditional novelty 2.0 of 10

    This paper is a broad tutorial on applying LAMs and agentic AI to 6G, largely restating existing research rather than introducing new results.

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