Abstract
Sixth-generation (6G) networks are anticipated to achieve transformative advancements, characterized by extreme connectivity, deep integration with artificial intelligence (AI) and sensing, and airground integration. The evolution of 6G exhibits two major trends: ubiquitous intelligence and sustainability. The former aims to embed state-of-the-art AI technology into the 6G network, from the physical layers to applications, while the latter emphasizes reducing energy consumption while enhancing network performance to address environmental concerns. Despite the amazing progress in recent years, AI advancements come with substantial increases in data and computational overhead, posing critical challenges for integrating AI into sustainable 6G networks. First, high energy consumption from large datasets and heavyweight AI models contradicts 6G’s green goals. Second, the precise collection of large datasets, message delivery latency, and inference delays in AI models pose challenges for real-time tasks in 6G. Third, the uninterpretability and unpredictability of AI models complicate meeting the stringent requirements for controllable transmission in dynamic wireless environments. Addressing these challenges and achieving sustainable 6G with ubiquitous intelligence calls for a revolutionary design of 6G architecture and AI frameworks. To this end, this paper introduces a novel and practical methodology for green, real-time, and controllable 6G native intelligence, starting with knowledge graph (KG) analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins (DTs) to create precise replicas of physical 6G networks. This leads to a pervasive multi-level (PML)-AI framework supported by a task-centric, three-layer 6G architecture. The AI framework operates through non-real-time and real-time cycles, leveraging three key technologies: wireless data KGs for efficient data management, lightweight AI models for sub-millisecond real-time responsiveness, and DTs for AI pre-validation. A prototype system is built on the proposed 6G architecture and PML-AI framework, and experimental results show that data overhead is significantly reduced and real-time intelligence at the millisecond level can be realized.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 62225107), Natural Science Foundation on Frontier Leading Technology Basic Research Project of Jiangsu (Grant No. BK20222001), and Fundamental Research Funds for the Central Universities (Grant No. 2242022k60002).
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Xiaohu YOU is a member of the Chinese Academy of Sciences and the chief professor at Southeast University. He is also the director and chief scientist of Purple Mountain Laboratories, the deputy director of Pengcheng Laboratory, and the director of the National Mobile Communications Research Laboratory. As a leading scientist in mobile communications, he has been the chief expert for the National 863 Programs of 3G, 4G, and 5G research in China during the past 30 years, and currently he serves as the chief expert for the National Key R&D Program on Broadband Communication and New Networks as well as the chief expert for the Ministry of Science and Technology’s Program on 6G. He has led the development strategy research on mobile communication, drafted its holistic technical framework, promoted the advance of broadband mobile communication technology in China, and made remarkable contributions to telecommunication technology research and development in China.
Prof. You has achieved a number of crucial accomplishments in communication theory and technologies, and holds over 100 domestic and international invention patents. He has made original innovation contributions to the capacity-approaching theory on broadband wireless transmission and its wide-scope engineering applications, which won the First Class National Technological Invention Award. He has also made pioneering contributions to distributed MIMO and cell-free wireless transmission and received the IET Achievement Medal and the Tan Kah Kee Science Award. He also achieved critical breakthroughs in high-frequency wireless transmission with large-scale integrated phased arrays and promoted their widespread applications in industry, which won the Second Class National Technological Invention Award. His research on multipath channel modeling and corresponding transmission methods has been widely validated and applied in industry, which won the Second Class National Science and Technology Progress Award.
He has published over 500 academic papers in top international journals with around 30000 citations. His research papers on 5G and 6G wireless transmission and system architecture, published in SCIENCE CHINA Information Sciences, are among the most highly cited academic papers globally. He authored the world’s first academic monograph on distributed MIMO and cell-free mobile communications. He is the recipient of the IEEE Communications Society Fred W. Ellersick Best Paper Award, as well as three Best Paper Awards at top international conferences, such as IEEE GLOBECOM and IEEE WCNC. He also received three Outstanding Paper Awards in the fields of electronics, information, and communication in China.
In recognition of his outstanding contributions to mobile communications in China, he was awarded the National May 1 Labor Medal and the title of the National Outstanding Science and Technology Worker. Renowned in the international academic community, he was elected an IEEE Fellow in 2011 and has served as the general chair of prominent international conferences such as IEEE WCNC 2013 and IEEE ICC 2019. He has always remained dedicated to the forefront of scientific research and teaching, and cultivated a large number of scholars and experts in both academic and industrial societies.
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You, X., Huang, Y., Zhang, C. et al. When AI meets sustainable 6G. Sci. China Inf. Sci. 68, 110301 (2025). https://doi.org/10.1007/s11432-024-4257-6
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DOI: https://doi.org/10.1007/s11432-024-4257-6
