W3: 6G-Enabled Large Models: Architecture, Optimization, and Deployment

Co-Organizer: Guangyi Liu, China Mobile Research Institute, China
Co-Organizer: Yang Yang, The Hong Kong University of Science and Technology, China
Co-Organizer: Chenghui Peng, Huawei Technologies Co., Ltd., China
Co-Organizer: Nan Cheng, Xidian University, China

Abstract: With the rapid advancement of artificial intelligence (AI) technologies, particularly the rise of large models such as Deepseek and GPT, it is foreseeable that a huge number of AI phones deploying large models will emerge in the near future. The deployment, inference, and updating of these large models require ubiquitous massive computing power, ultra-low latency connectivity, and ultra-high bandwidth data transmission. This poses new communication and beyond communication capability requirements for future wireless network. The 6G network is expected to integrate multidimensional resource elements such as communication, sensing, computing, and intelligence, natively supporting intelligent orchestration, control, and scheduling of various AI applications. This will achieve comprehensive integration of the network and AI, supporting large-scale model operations across various industries to meet the growing demands of AI applications. However, to fully harness the potential of 6G in empowering large AI models, key challenges such as network architecture design, multi-dimensional resource management, protocol development, and security risks must be
addressed.

Program Schedule:

11:00-11:18: Research on the Application of AI Large Models in 6G > Network Operations and Maintenance 

11:18-11:36: MAHs: Multi-Adaptation Hubs for Online Learning in Large > Network Models 

11:36-11:54: End-to-End Edge AI Service Provisioning Framework in 6G ORAN 

11:54-12:12: Multi-objective AI Service Quality Optimization for > Generative AI Inference in Native AI Wireless Networks 

12:12-12:30: Multivariate Time Series Prediction with Quantum Tiny Time > Mixer in Mobile Networks

End.

Co-Organizer’s Bios:

Guangyi Liu

Dr. Guangyi Liu, Chief Scientist and 6G Director of China Mobile Communication Corporation (CMCC), Co-chair of 6G Alliance of Network AI (6GANA), Vice chair of THz industry alliance in China, cochair of the wireless technology working group of IMT-2030 (6G) promotion group supported by Ministry of Information and Industry Technology of China. He is leading the 6G R&D of CMCC since 2018. During 2014~2020, he led the research, standardization and industrialization 5G in CMCC; During 2006~2016, he led the research, standardization and industrialization of 4G’s evolution. He was awarded “national innovation award in science and technology of 2016” of Chinese government due to the contribution to TD-LTE standardization and industrialization globally. In 2009, 2013 and 2017, he received “excellent patent awards” from the Chinese Intellectual property office. He has published more than 150 papers and filed more than 500 patents.

Yang Yang

Dr. Yang Yang, IEEE Fellow, is the Associate Vice President (Teaching& Learning) at The Hong Kong University of Science and Technology (Guangzhou), China. He is also an adjunct professor with the Department of Broadband Communication at Peng Cheng Laboratory and a Senior Consultant for Shenzhen Smart City Technology Development Group, China. Before joining
HKUST(Guangzhou), he has held faculty positions at the Chinese University of Hong Kong, Brunel University, U.K., University College London (UCL), U.K., CAS-SIMIT, and ShanghaiTech University,
China. He is the Co-chair of the workshop on Architectural Innovations for 6G Native-AI and Digital Twin in IEEE ICC 2023. Yang’s research interests include multi-tier computing networks, 5G/6G
systems, AIoT technologies, intelligent services and applications, and advanced wireless testbeds. He has published more than 300 papers and filed more than 120 technical patents in these research areas.

Chenghui Peng

Chenghui Peng, Chief expert on 6G Network AI of Wireless Technology Lab, Huawei, and leading the research of 6G native AI network architecture, task centric, deep integration of communication
and compute, distributed wireless AI/ML, etc. He is co-leader of 6G AI native network architecture and 6G RAN architecture projects in IMT2030. He joined Huawei in 2001, and led the research of 5G network slicing during 2015~2019. He has filed more than 200 patents.

Nan Cheng

Nan Cheng (Senior Member, IEEE) received the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Waterloo in 2016, and B.E. degree and the M.S. degree from the Department of Electronics and Information Engineering, Tongji University, Shanghai, China, in 2009 and 2012, respectively. He worked as a Postdoctoral fellow with the Department of Electrical and Computer Engineering, University of Toronto, from 2017 to 2019. He is currently a professor with State Key Lab. of ISN and with School of Telecommunications Engineering, Xidian University, Shaanxi, China. He has published over 90 journal papers in IEEE Transactions and other top journals. He serves as associate editors for IEEE Transactions on Vehicular Technology, IEEE Open Journal of the Communications Society, and Peer-to-Peer Networking and Applications, and serves/served as guest editors for several journals. His current research focuses on B5G/6G, AI-driven future networks, and space-air-ground integrated network.