Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities
Qimei Cui, Xiaohu You, Ni Wei, Guoshun Nan, Xuefei Zhang, Jianhua Zhang, Xinchen Lyu, Ming Ai, Xiaofeng Tao, Zhiyong Feng, Ping Zhang, Qingqing Wu, Meixia Tao, Yongming Huang, Chongwen Huang, Guangyi Liu, Chenghui Peng, Zhiwen Pan, Tao Sun, Dusit Niyato, Tao Chen, Muhammad Khurram Khan, Abbas Jamalipour, Mohsen Guizani, Chau Yuen
TL;DR
This paper surveys AI integration with 6G networks, identifying three progressive stages—AI4NET, NET4AI, and AIaaS—and detailing driving forces, architecture, and enabling technologies such as Distributed Intelligence, Digital Twins, SMPC, and semantic communication. It analyzes standardization efforts (3GPP, ITU-T, ETSI) and key challenges including reliability, real-time AI, sustainability, security, and human-AI collaboration, while discussing the emerging concept of Wireless Network Large Models to support AI-driven network operations. The work highlights a future where 6G provides AI-enabled services across immersive, industrial, medical, and transportation domains, leveraging computing-power networks and edge-cloud ecosystems to achieve ubiquitous intelligence. Overall, the paper offers a framework and roadmap for realizing AI-native 6G through three stages, with detailed considerations for data, computation, security, and orchestration across the network stack.
Abstract
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.