mlsanityData to 14 Oct 2026

AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems

arXiv 2311.05842 ↗First posted 10 Nov 2023cs.NI cs.AIcited by 19

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Abstract (arXiv)

The evolution towards 6G architecture promises a transformative shift in communication networks, with artificial intelligence (AI) playing a pivotal role. This paper delves deep into the seamless integration of Large Language Models (LLMs) and Generalized Pretrained Transformers (GPT) within 6G systems. Their ability to grasp intent, strategize, and execute intricate commands will be pivotal in redefining network functionalities and interactions. Central to this is the AI Interconnect framework, intricately woven to facilitate AI-centric operations within the network. Building on the continuously evolving current state-of-the-art, we present a new architectural perspective for the upcoming generation of mobile networks. Here, LLMs and GPTs will collaboratively take center stage alongside traditional pre-generative AI and machine learning (ML) algorithms. This union promises a novel confluence of the old and new, melding tried-and-tested methods with transformative AI technologies. Along with providing a conceptual overview of this evolution, we delve into the nuances of practical applications arising from such an integration. Through this paper, we envisage a symbiotic integration where AI becomes the cornerstone of the next-generation communication paradigm, offering insights into the structural and functional facets of an AI-native 6G network.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 67 of its 143 references that are in the map. First posted on arXiv 10 Nov 2023.

Builds on

67 of 143 references.

References · 67

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 15; 2025: 1; 2026: 2.

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  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  2. 2024WirelessLLM: Empowering Large Language Models Towards Wireless Intelligencecited by 44
  3. 2024TelecomGPT: A Framework to Build Telecom-Specfic Large Language Modelscited by 40
  4. 2024Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6Gcited by 31
  5. 2024Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systemscited by 27
  6. 2024TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specificationscited by 27
  7. 2024Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detectioncited by 16
  8. 2024ENWAR: A RAG-empowered Multi-Modal LLM Framework for Wireless Environment Perceptioncited by 7
  9. 2023Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunitiescited by 5
  10. 2024Follow-Me AI: Energy-Efficient User Interaction with Smart Environmentscited by 4
  11. 2024Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specificationscited by 3
  12. 2024NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Modelscited by 3
7 more
  1. 2024Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networkscited by 2
  2. 2025BERT4beam: Large AI Model Enabled Generalized Beamforming Optimizationcited by 1
  3. 2026TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunicationscited by 1
  4. 2026Neural Router: Semantic Content Matching for Agentic AIcited by 1
  5. 2024Future-Proofing Mobile Networks: A Digital Twin Approach to Multi-Signal Managementcited by 0
  6. 2024Shavette: Low Power Neural Network Acceleration via Algorithm-level Error Detection and Undervoltingcited by 0
  7. 2024Collaborative Participatory Research with LLM Agents in South Asia: An Empirically-Grounded Methodological Initiative and Agenda from Field Evidence in Sri Lankacited by 0