mlsanityData to 14 Oct 2026

Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions

arXiv 2308.06250 ↗First posted 11 Aug 2023cs.NI eess.SPcited by 32

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Abstract (arXiv)

Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream tasks can be accomplished with only few-shot or even zero-shot learning and exhibit state-of-the-art performances. As widely envisioned, the big AI models are to rapidly penetrate into major intelligent services and applications, and are able to run at low unit cost and high flexibility. In 6G wireless networks, to fully enable intelligent communication, sensing and computing, apart from providing other intelligent wireless services and applications, it is of vital importance to design and deploy certain wireless BAIMs (wBAIMs). However, there still lacks investigation on architecture design and system evaluation for wBAIM. In this paper, we provide a comprehensive discussion as well as some in-depth prospects on the demand, design and deployment aspects of the wBAIM. We opine that wBAIM will be a recipe of the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions. Then, we provide the core characteristics, principles, and pilot studies to guide the design of wBAIMs, and discuss the key aspects of developing wBAIMs through identifying the differences between the existing BAIMs and the emerging wBAIMs. Finally, related research directions and potential solutions are outlined.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Aug 2023.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 6; 2025: 16; 2026: 6.

32 papers.

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  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  2. 2023Large Language Model Enhanced Multi-Agent Systems for 6G Communicationscited by 53
  3. 2024Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detectioncited by 16
  4. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 14
  5. 20256G WavesFM: A Foundation Model for Sensing, Communication, and Localizationcited by 13
  6. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  7. 2026ComAgent: Multi-LLM based Agentic AI Empowered Intelligent Wireless Networkscited by 8
  8. 2023Toward 6G Native-AI Network: Foundation Model based Cloud-Edge-End Collaboration Frameworkcited by 7
  9. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
  10. 2024FedsLLM: Federated Split Learning for Large Language Models over Communication Networkscited by 3
  11. 2025Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localizationcited by 3
  12. 2026Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Surveycited by 3
20 more
  1. 2025Large AI Model for Delay-Doppler Domain Channel Prediction in 6G OTFS-Based Vehicular Networkscited by 2
  2. 2025Edge Large AI Models: Revolutionizing 6G Networkscited by 2
  3. 2025AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Modelcited by 2
  4. 2025From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communicationscited by 2
  5. 2025Agentic TinyML for Intent-aware Handover in 6G Wireless Networkscited by 2
  6. 2024Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learningcited by 1
  7. 2025Deploying Large AI Models on Resource-Limited Devices with Split Federated Learningcited by 1
  8. 2025Towards Efficient Federated Learning of Networked Mixture-of-Experts for Mobile Edge Computingcited by 1
  9. 2026Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Modelscited by 1
  10. 2023Semantic Information Extraction for Text Data with Probability Graphcited by 0
  11. 2024Personalized Hierarchical Split Federated Learning in Wireless Networkscited by 0
  12. 2025CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systemscited by 0
  13. 2025Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Designcited by 0
  14. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  15. 2025Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systemscited by 0
  16. 2025Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networkscited by 0
  17. 2025Large Speech Model Enabled Semantic Communicationcited by 0
  18. 2026TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networkscited by 0
  19. 2026SpikeWFM: Spiking-Aided Wireless Foundation Model for Robust Channel Predictioncited by 0
  20. 2026Wireless Foundation Models: State-of-the-Art and Open Challengescited by 0