Special Issue on Artificial Intelligence-Native Radio Access Networks (AI-RAN): Foundations, Methodologies, and Applications
Published 16 May, 2025
Artificial intelligence (AI)-native radio access networks (AI-RAN) represent a transformative direction in the evolution of mobile networks towards the sixth-generation (6G) wireless networks and beyond. With the explosive growth of mobile data traffic, the proliferation of intelligent devices, and the diversification of application scenarios (e.g., XR, digital twin, vehicular networks), traditional wireless access network architectures are increasingly challenged by system complexity, dynamic environments, and real-time requirements. AI-RAN aims to deeply integrate advanced artificial intelligence technologies—such as deep learning, reinforcement learning, graph intelligence, and large language models (LLMs)—into the core of RAN architectures. This integration enables end-to-end perception, reasoning, decision-making, and control capabilities that can adaptively optimize resource allocation, service delivery, and topology management in real time.
Recent research has demonstrated the significant potential of AI-RAN across multiple dimensions, including learning-based resource scheduling, traffic prediction, anomaly detection, network slicing, and proactive content caching. Emerging paradigms—such as multi-agent systems, digital twins, and LLM-driven autonomous control—are shifting the role of AI in RAN from support and automation toward autonomy and cognition. The advancement of Open RAN (O-RAN) further provides a standardized and interoperable foundation for integrating intelligent functions (e.g., xApps, rApps) into the RAN ecosystem.
Despite these advances, several key challenges continue to limit the full realization of AI-RAN. These include the development of generalizable and interpretable AI models that are robust to non-stationary, multi-scale wireless environments; the design of distributed and multi-agent architectures capable of coordination among edge nodes under resource and communication constraints; the balancing of performance, latency, and energy efficiency in real-time AI inference and control; and the creation of high-fidelity simulators and open datasets that support large-scale training, benchmarking, and reproducible research for AI-RAN.
This special issue will focus on the theoretical foundations, architectural innovations, learning methodologies, and practical implementations of AI-RAN. It aims to provide a high-impact forum for academic researchers, industry practitioners, and standardization bodies to share new insights, report cutting-edge developments, and explore future directions in this rapidly evolving field.
The topics include but are not limited to:
- Theoretical frameworks and architectures for AI-RAN
- AI-RAN agents based on Large Language Models (LLMs) and foundation models
- Multi-agent reinforcement learning and decentralized control for RAN
- Graph neural networks (GNNs) for topology-aware network modeling and optimization
- Self-supervised, federated, and continual learning methods for wireless environments
- Semantic communications and goal-oriented transmission in AI-RAN
- QoE-driven video streaming and content delivery with AI-edge collaboration
- AI for O-RAN architecture: near-RT RIC, xApps, rApps, and SMO intelligence
- Dataset construction, open platforms, and reproducible experiments for AI-RAN
- Cross-layer AI designs spanning PHY, MAC, and RRM layers
- Digital twins and AI-RAN simulation platforms for offline training and real-time inference
- AI-driven security, privacy, and trust mechanisms in RAN
- AI-RAN for extreme scenarios: ultra-dense, ultra-reliable, low-latency, and energy-constrained systems
- Real-world testbeds, field trials, and benchmark performance evaluation
Manuscript Submission Information
The journal’s submission platform (Editorial Manager®) is now available for receiving submissions to this Special Issue. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI:AI-RAN” when submitting your manuscript online. Authors should consult the link for submission instructions: https://www.editorialmanager.com/intecom/default2.aspx. The Guide for Authors could be found on the Journal Homepage here: https://www.sciencedirect.com/journal/journal-of-information-and-intelligence/publish/guide-for-authors.
Timeline
- Submission deadline: September 15, 2025
- Acceptance deadline: October 31, 2025
- Publication Date: January 31, 2026
Guest Editors:
Chenxi Liu
Associate Professor, Beijing University of Posts and Telecommunications, China
Email: chenxi.liu@bupt.edu.cn
Chenxi Liu received his B.E. degree from Central South University, Changsha, China, in 2010, and Ph.D. degree from The University of New South Wales, Sydney, Australia, in 2016. From 2017 to 2019, he was a Postdoctoral research fellow in Singapore University of Technology and Design. Since 2019, he has been with the Beijing University of Posts and Telecommunications, where he is currently an Associate Professor. His research interests include unmanned aerial vehicle-enabled wireless networks and network intelligence. He received the Best Paper Awards from the IEEE ICC 2022, the WCSP 2023 and the IEEE/CIC ICCC 2024. He is currently serving as an Editor for the IEEE Wireless Communications Letters and Journal of Information and Intelligence.
Howard H. Yang
Assistant Professor & Research Fellow, Zhejiang University, China
Email: haoyang@intl.zju.edu.cn
Howard H. Yang received the B.E. degree in Communication Engineering from Harbin Institute of Technology (HIT), China, in 2012, and the M.Sc. degree in Electronic Engineering from Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2013. He earned the Ph.D. degree in Electrical Engineering from Singapore University of Technology and Design (SUTD), Singapore, in 2017. He was a Postdoctoral Research Fellow at SUTD from 2017 to 2020, a Visiting Postdoc Researcher at Princeton University from 2018 to 2019, and a Visiting Student at the University of Texas at Austin from 2015 to 2016. Currently, he is an assistant professor with the Zhejiang University/University of Illinois Urbana-Champaign Institute (ZJU-UIUC Institute), Zhejiang University, Haining, China. He is also an adjunct assistant professor with the Department of Electrical and Computer Engineering at the University of Illinois Urbana-Champaign, IL, USA.
Dr. Yang’s research interests cover various aspects of wireless communications, networking, and signal processing, currently focusing on the modeling of modern wireless networks, high dimensional statistics, graph signal processing, and machine learning. He serves as an Associate Editor for the IEEE Transactions on Wireless Communications and an Editor-at-Large for the IEEE Open Journal of The Communications Society. He received the IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award in 2023, the IEEE Signal Processing Society Best Paper Award in 2022, the IEEE WCSP 10-Year Anniversary Excellent Paper Award in 2019, and the IEEE WCSP Best Paper Award in 2014.
Kun Guo
Research Professor, East China Normal University, China
Email: kguo@cee.ecnu.edu.cn
Kun Guo received the B.E. degree in Telecommunications Engineering from Xidian University, Xi'an, China, in 2012, where she received the Ph.D. degree in communication and information systems in 2019. From 2019 to 2021, she was a Post-Doctoral Research Fellow with the Singapore University of Technology and Design (SUTD), Singapore. Currently, she is a Research Professor with the School of Communications and Electronics Engineering at East China Normal University, Shanghai, China. Her research interests focus on wireless edge computing and intelligence.
Wenchao Xia
Associate Professor, Nanjing University of Posts and Telecommunications, China
Email: xiawenchao@njupt.edu.cn
Wenchao Xia received his B.S. degree in communication engineering and Ph.D. degree in communication and information systems from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2014 and 2019, respectively. From 2019 to 2020, he was a Postdoctoral Research Fellow with Singapore University of Technology and Design, Singapore. He is currently with the faculty of the Jiangsu Key Laboratory of Wireless Communications, College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications. His research interests include edge intelligence and mobile IoT.
He was a recipient of the IEEE Globecom Best Paper Award in 2016 and the IEEE JC&S Best Paper Award in 2022. He serves as an Associate Editor for the IEEE Wireless Communications Letters and IET Electronics Letters.
Chenyuan Feng
Research Fellow, University of Exeter, U.K.
Email: c.feng@exeter.ac.uk
Chenyuan Feng received the B.E. degree in electrical and electronics engineering from the University of Electronic Science and Technology of China (UESTC), Chengdu, China, in 2016, and the Ph.D. degree in information system technology and design from Singapore University of Technology and Design (SUTD), Singapore, in 2021, respectively. Currently she is a research fellow at Department of Computer Science，University of Exeter, U.K.. Her research interests include edge intelligence, multimedia intelligence, as well as AI for network and communication. Dr. Feng is a receipt of 2021 IEEE ComComAp Best Paper Award, 2024 IEEE ICCT Best Paper Award, and 2025 IEEE ICCCS 10th Anniversary Best Paper Award. Dr. Feng was invited to deliver several tutorials and invited talk at International conferences in the area of machine learning for communication, such as IEEE PIMRC'24, IEEE VCC'24, IEEE ICCT'22, ICCT'24 and IEEE VTC-Sping'25. Dr. Feng serves as an Associate Editor for the IEEE Internet of Things Journal and the IEEE Open Journal of the Communications Society. Dr. Feng is a Marie Skłodowska-Curie Scholar and 6G Rising Star Young Scientist.
Tony Q. S. Quek
Fellow of the Singapore Academy of Engineering (SAEng)
Professor, Singapore University of Technology and Design, Singapore
Email: tonyquek@sutd.edu.sg
Tony Q. S. Quek received the B.E. and M.E. degrees in electrical and electronics engineering from the Tokyo Institute of Technology in 1998 and 2000, respectively, and the Ph.D. degree in electrical engineering and computer science from the Massachusetts Institute of Technology in 2008. Currently, he is the Associate Provost for AI and Digital Innovation at Singapore University of Technology and Design (SUTD), the Cheng Tsang Man Chair Professor, and ST Engineering Distinguished Professor. He also serves as the Director of the Future Communications R&D Programme, the Head of ISTD Pillar, and the Deputy Director of the SUTD-ZJU IDEA. His current research topics include wireless communications and networking, network intelligence, non-terrestrial networks, open radio access network, and 6G.
Dr. Quek has been actively involved in organizing and chairing sessions, and has served as a member of the Technical Program Committee as well as symposium chairs in a number of international conferences. He is currently serving as an Area Editor for the IEEE Transactions on Wireless Communications.
Dr. Quek was honored with the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, the 2015 SUTD Outstanding Education Awards -- Excellence in Research, the 2016 IEEE Signal Processing Society Young Author Best Paper Award, the 2017 CTTC Early Achievement Award, the 2017 IEEE ComSoc AP Outstanding Paper Award, the 2020 IEEE Communications Society Young Author Best Paper Award, the 2020 IEEE Stephen O. Rice Prize, the 2020 Nokia Visiting Professor, and the 2022 IEEE Signal Processing Society Best Paper Award. He is the AI on RAN Working Group Chair in AI-RAN Alliance. He is a Fellow of IEEE, a Fellow of WWRF, and a Fellow of the Academy of Engineering Singapore.