mlsanityData to 7 Oct 2026

Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities

arXiv 2412.14538 ↗First posted 19 Dec 2024cs.NI cs.AI eess.SPcited by 25

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Abstract (arXiv)

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for Network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, Network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. In the final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 4 of its 21 references that are in the map. First posted on arXiv 19 Dec 2024.

Builds on

4 of 21 references.

References · 4

  1. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  2. 2023Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directionscited by 21
  3. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  4. 2022A Survey on the Network Models applied in the Industrial Network Optimizationcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 14; 2026: 11.

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  1. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 13
  2. 2025Large AI Models for Wireless Physical Layercited by 6
  3. 2025AURA: Adaptive Unified Reasoning and Automation with LLM-Guided MARL for NextG Cellular Networkscited by 2
  4. 2025Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networkscited by 2
  5. 2025Reflection-Driven Self-Optimization 6G Agentic AI RAN via Simulation-in-the-Loop Workflowscited by 2
  6. 2026A Survey on AI for 6G: Challenges and Opportunitiescited by 2
  7. 2026Adaptive 3D-RoPE: Physics-Aligned Rotary Positional Encoding for Wireless Foundation Modelscited by 2
  8. 2025Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Schedulingcited by 1
  9. 2026In-Context Source and Channel Codingcited by 1
  10. 2026A Token/KV-Cache Communication Media Selection and Resource Allocation Strategy for Multi-Agent Collaborationcited by 1
  11. 2025Empowering Large Language Models in Wireless Communication: A Novel Dataset and Fine-Tuning Frameworkcited by 0
  12. 2025Advancing Compositional LLM Reasoning with Structured Task Relations in Interactive Multimodal Communicationscited by 0
13 more
  1. 2025Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networkscited by 0
  2. 2025Towards 6G Intelligence: The Role of Generative AI in Future Wireless Networkscited by 0
  3. 2025Where 6G Stands Today: Evolution, Enablers, and Research Gapscited by 0
  4. 2025Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Surveycited by 0
  5. 2025AI/ML in 3GPP 5G Advanced -- Services and Architecturecited by 0
  6. 2025Agentic AI for ISAC: Analysis, Framework, and Case Studycited by 0
  7. 2026Cross-reality location privacy protection in 6G-enabled vehicular metaverses: an LLM-enhanced hybrid generative diffusion model-based approachcited by 0
  8. 2026CORE:Toward Ubiquitous 6G Intelligence Through Collaborative Orchestration of Large Language Model Agents Over Hierarchical Edgecited by 0
  9. 2026Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factorycited by 0
  10. 2026Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directionscited by 0
  11. 2026E3-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inferencecited by 0
  12. 2026Towards CSI-Native Foundation Models: A Channel-Adaptive Roadmap for 6Gcited by 0
  13. 2026A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networkscited by 0