mlsanityData to 7 Oct 2026

Towards Intent-Based Network Management: Large Language Models for Intent Extraction in 5G Core Networks

arXiv 2403.02238 ↗First posted 4 Mar 2024cs.NI cs.AIcited by 15

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Abstract (arXiv)

The integration of Machine Learning and Artificial Intelligence (ML/AI) into fifth-generation (5G) networks has made evident the limitations of network intelligence with ever-increasing, strenuous requirements for current and next-generation devices. This transition to ubiquitous intelligence demands high connectivity, synchronicity, and end-to-end communication between users and network operators, and will pave the way towards full network automation without human intervention. Intent-based networking is a key factor in the reduction of human actions, roles, and responsibilities while shifting towards novel extraction and interpretation of automated network management. This paper presents the development of a custom Large Language Model (LLM) for 5G and next-generation intent-based networking and provides insights into future LLM developments and integrations to realize end-to-end intent-based networking for fully automated network intelligence.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

3 of 21 references.

References · 3

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2022Towards Supporting Intelligence in 5G/6G Core Networks: NWDAF Implementation and Initial Analysiscited by 7
  3. 2022An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterizationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 5; 2026: 6.

15 papers.

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  1. 2024A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directionscited by 25
  2. 2024Leveraging Large Language Models for Integrated Satellite-Aerial-Terrestrial Networks: Recent Advances and Future Directionscited by 10
  3. 2024Semantic Routing for Enhanced Performance of LLM-Assisted Intent-Based 5G Core Network Management and Orchestrationcited by 7
  4. 2024LINKs: Large Language Model Integrated Management for 6G Empowered Digital Twin NetworKscited by 7
  5. 2025LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approachcited by 5
  6. 2025Towards End-to-End Network Intent Management with Large Language Modelscited by 4
  7. 2025Generative AI for Intent-Driven Network Management in 6G RAN: A Case Study on the Mamba Modelcited by 1
  8. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  9. 2025Efficient Asynchronous Federated Evaluation with Strategy Similarity Awareness for Intent-Based Networking in Industrial Internet of Thingscited by 0
  10. 2026Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networkscited by 0
  11. 2026IntAgent: NWDAF-Based Intent LLM Agent Towards Advanced Next Generation Networkscited by 0
  12. 2026Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systemscited by 0
3 more
  1. 2026Engineering Trustworthy Agentic AI for Critical Systemscited by 0
  2. 2026Benchmarking LLM-Guided Control-Plane Policies for Backend Fault Isolation in HAProxycited by 0
  3. 2026Advanced LLM-Enhanced Intent-Based 5G Network Management using Dynamic Semantic Routescited by 0