Wednesday 14 September, 14:00-15:30 (UTC+9)
The 5G began with the first version of Rel-15 in 2018 and has achieved great technical success through three releases of evolution. Since Rel-18 in 2022, the 5G has leapt into the 5G-Advanced era. As one of the most important revolutionary technologies, native intelligence including AI/ML for air-interface, AI/ML for NG-RAN and 5G system support for AI/ML-based services will be investigated in Rel-18. AI/ML functionalities will be implemented into diverse network entities and protocol layers in 5G- Advanced. 6G will boast its native AI capability. Its air interface and network designs will leverage E2E AI and machine learning to implement customized optimization and automated O&M. Not only this, each 6G network element will natively integrate communication, computing, and sensing capabilities, facilitating the evolution from centralized intelligence in the cloud to ubiquitous intelligence on deep edges. A distributed machine learning architecture built on deep edge intelligence will be key to meeting the large-scale intelligence requirements of future society and manufacturing. How to embrace AI/ML in 5G-Advanced and the road to 6G require our industry and academic deep investigation. There are many challenges including the performance gain for air interface by using AI/ML model compared with the traditional algorithm, complexity for AL/ML training and interring, AI/ML model storage and transmission efficiency, multi-vendors AI/ML model Interoperation and etc. Therefore, it will be very timely to investigate how native intelligence evolves towards 5G-Advanced and 6G from technology points of view, taking into accounts the technical capabilities, key challenges, and potential trends. In this panel, we will invite pioneer experts from the mobile industry. The panel can serve as a good opportunity to share the experts’ views about native intelligence for 5G-advanced and 6G and can provide a bridge between industry and academia.
- For wireless communication in the next decade, will AI/ML algorithm replace most of traditional wireless communication algorithm or not?
- For 5G-Advanced, what are important use cases and corresponding advantages by using AI/ML.
- For 6G, what’s addtional use cases and cooresponding advantages by using AI/ML
- What’s the most challenge area for AI/ML applying to wireless network?
- Wireless communication typical involve multi-vendors interoperation. How to handle multi-vendors interoperation considering diverse machine learing framework in the current AI/ML industry?
Zhenfei Tang (Senior Manager of Huawei Wireless Research, Huawei, China)
List of Panelists
Erik Dahlman (Senior Expert of Radio Access Technologies with Ericsson Research, Ericsson, Sweden)
Tingfang Ji (VP of Engineering, Wireless R&D, Qualcomm)
Jakob Hoydis (Principal Research Scientist, NVIDIA)
Jianmin Lu (Executive Director, Huawei Wireless Technology Lab)
Zhenfei Tang is the senior manager of the 5G evolution research in Huawei. He led 5G evolution research and managed a series of national projects from Huawei. He has been engaged in the research for 3G, 4G and 5G wireless communications systems and was the major representative of Huawei in 3GPP and in China IMT-Advanced since he joined Huawei Technologies Co., Ltd in 2005. Mr. Tang has more than 100 granted patents in the area of wireless communications and contributed more than 500 papers to international standards and conferences. He received his M.S. degree from Beijing University of Posts and Telecommunications in 2004.
Erik Dahlman joined Ericsson in 1993 and is currently Senior Expert in Radio Access Technologies within Ericsson Research. He has been involved in the development of wireless access technologies from early 3G, via 4G LTE, and most 5G NR. He is currently focusing on the evolution of 5G as well as technologies applicable to beyond 5G wireless access. He is the co-author of the books 3G Evolution – HSPA and LTE for Mobile Broadband, 4G – LTE and LTE-Advanced for mobile broadband, 4G – LTE-Advanced Pro and The Road to 5G and, most recently, 5G NR – The Next Generation Wireless Access Technology. He has a PhD in Telecommunication from the Royal Institute of Technology
Tingfang Ji joined Qualcomm in 2003 and is currently a VP of Engineering on Wireless Research. He leads a flagship research project on 5G NR design/standardization, pre-commercial IODT/trials, experimental macro networks, and long-term pre-6G research. Before joining Qualcomm, Tingfang was a member of the technical staff at Bell Labs. As an inventor, he has more than 600 granted US patents. Tingfang received his Ph.D. degree in E.E. from the University of Michigan, Ann Arbor in 2001, and also received a B.Sc. from Tsinghua University, Beijing.
Jakob Hoydis is a Principal Research Scientist at NVIDIA working on the intersection of machine learning and wireless communications. Prior to this, he was Head of a research department at Nokia Bell Labs, France, and co-founder of the social network SPRAED. He obtained the diploma degree in electrical engineering from RWTH Aachen University, Germany, and the Ph.D. degree from Supéléc, France. From 2019-2021, he was chair of the IEEE COMSOC Emerging Technology Initiative on Machine Learning as well as Editor of the IEEE Transactions on Wireless Communications. Since 2019, he is Area Editor of the IEEE JSAC Series on Machine Learning in Communications and Networks. He is recipient of the 2019 VTG IDE Johann-Philipp-Reis Prize, the 2019 IEEE SEE Glavieux Prize, the 2018 IEEE Marconi Prize Paper Award, the 2015 IEEE Leonard G. Abraham Prize, the IEEE WCNC 2014 Best Paper Award, the 2013 VDE ITG Förderpreis Award, and the 2012 Publication Prize of the Supéléc Foundation. He has received the 2018 Nokia AI Innovation Award, as well as the 2018 and 2019 Nokia France Top Inventor Awards. He is a co-author of the textbook “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency” (2017). He is one of the maintainers and core developers of Sionna, a GPU-accelerated open-source link-level simulator for next-generation communication systems.
Jianmin Lu joined the Huawei Technologies in 1999. During the last two decades, he conducted various researches on wireless communications especially on physic layer and MAC layer and developed 3G, 4G and 5G products. He received more than 50 patents during the research. He was deeply involved in 3GPP2 (EVDO/UMB), WiMAX/802.16m and 3GPP (LTE/NR) standardization and contributed several key technologies such as flexible radio frame structure, radio resource management and MIMO. His current research interest is in the area of signal processing, protocol and networking for the next generation wireless communication. He is currently Executive Director of Huawei Wireless Technology Lab.