应我校电子与信息工程学院孟维晓教授、陈舒怡助理教授邀请,在《6G先进无线通信与网络技术》高端外国专家引进计划的资助下,在哈尔滨工业大学国际合作部、电信学院青年教师联合会、IEEE ComSoc哈尔滨分会、IEEE VTS哈尔滨分会的支持下,世界著名无线通信专家IEEE Life Fellow日本东北大学教授Fumiyuki Adachi安达文幸),中国工程院外籍院士英国皇家工程院院士IEEE Fellow英国肯特大学教授Jiangzhou Wang(王江舟),新加坡工程院院士IEEE Fellow Tony Quek教授,纽芬兰纪念大学计算机系主任Cheng Li(李成)教授以及新西兰奥克兰理工大学计算机和数学科学院副院长Peter Chong教授进行6G先进无线通信与网络技术高端外国专家系列讲座。

系列讲座第二期将于202312201330-1700在线下线上联合举行。线下讲座地点:哈工大科学园科技创新大厦(TIB) 21楼黄大年多功能厅。线上讲座地点:腾讯会议958-291-991,会议密码:1220。欢迎感兴趣的老师和同学参加。


讲座1Underwater Acoustic Communications and Networking for the Internet of Underwater Things (IoUT)


摘要: The Internet of Underwater Things (IoUT), a new class of the Internet of Things (IoT), has emerged in recent years as a network of smart interconnected underwater objects to create a worldwide network that will digitally link the oceans, rivers, and lakes. There have been a number of research and development initiatives tackling technical challenges of the IoUT. These are still in the early stage of investigation. The most challenging remaining tasks include efficient and reliable communication and networking, energy efficiency, and cost reduction. In this talk, we present an overview of IoUT and its applications with the focuses on the offshore oil and gas industry and marine transportation and fishery industry. We review the challenges faced by the marine monitoring and sensing applications, and introduce underwater communications and networking as the key enabling technologies for the IoUT. We present large-scale array network as the technical solutions for such problems by showing how this can be integrated with the passive network sensing and proactive network sensing approaches. We conclude the talk by reviewing the technical challenges of the underwater communications and networking.



Cheng Li received his B. Eng. and M. Eng. degrees from Harbin Institute of Technology, Harbin, P. R. China, in 1992 and 1995, respectively, and his Ph.D. degree in Electrical and Computer Engineering from Memorial University, St. John's, Canada, in 2004. He is currently a Full Professor and Head of the Department of Electrical and Computer Engineering of Memorial University, St. John's, Canada.

His research interests include wireless communications and networking, communications signal processing, underwater communication and networking, and mobile computing and computer networks. He has contributed 4 book chapters and over 350 research articles, including more than 150 journal publications. He is an IEEE Communications Society Distinguished Lecturer for the 2021-22 term.

He is an associate editor of the IEEE Transactions on Communications, the IEEE Internet-of-Things Journal, and the IEEE Network Magazine. He has served as the General Co-Chair of the ICNC'23, Q2SWinet’20, WINCOM'19, and AICON'19, and the TPC Co-Chair for the ICNC'20, ADHOCNETS’19, ACOSIS’19, WiCON’17, MSWiM'14, WiMob'11 and QBSC'10.

Dr. Li is the recipient of the Dean’s Award for Research Excellence at Memorial University in 2021, the recipient of the Technical Achievement Award of the IEEE Communications Society Communications Software Technical Committee in 2018, and the recipient of several Best Paper Awards at international conferences, including the IEEE Globecom'2017 and ICC'2010. He is a registered Professional Engineer (P.Eng.) in Canada and is a Senior Member of the IEEE and a member of the IEEE Communication Society, Computer Society, Vehicular Technology Society, and Ocean Engineering Society.


讲座2A Look at Federated Learning from Different Perspectives


摘要: Machine learning is playing an increasingly crucial role in the field of wireless communications due to its high efficiency in dealing with complex computations and models. In this talk, we provide a comprehensive coverage of a distributed learning paradigm based on federated learning, including the general architecture, model training algorithm, and analytical framework that quantifies the convergence rate. Specifically, we investigate federated learning from different perspectives, namely, wireless federated learning, over-the-air federated learning, data heterogeneity, and learning efficiency. Furthermore, we share some of our recent works in this area to provide different innovative ways of looking at this federated learning and tackling the inherent challenges of this technology.



Tony Q.S. Quek received the B.E. and M.E. degrees in Electrical and Electronics Engineering from Tokyo Institute of Technology, respectively. At Massachusetts Institute of Technology, he earned the Ph.D. in Electrical Engineering and Computer Science. Currently, he is the Cheng Tsang Man Chair Professor with Singapore University of Technology and Design (SUTD) and ST Engineering Distinguished Professor. He also serves as the Head of ISTD Pillar, Director for Future Communications R&D Programme, Sector Lead for SUTD AI Program, and the Deputy Director of SUTD-ZJU IDEA. His current research topics include wireless communications and networking, 6G, network intelligence, non-terrestrial networks, and open radio access network.

Dr. Quek has been actively involved in organizing and chairing sessions and has served as a TPC member in numerous international conferences. He is currently serving as an Area Editor for the IEEE Transactions on Wireless Communications. He was an Executive Editorial Committee Member of the IEEE Transactions on Wireless Communications, an Editor of the IEEE Transactions on Communications, and an Editor of the IEEE Wireless Communications Letters.

Dr. Quek received the 2008 Philip Yeo Prize for Outstanding Achievement in Research, the 2012 IEEE William R. Bennett Prize, 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 Professorship, and the the 2022 IEEE Signal Processing Society Best Paper Award. He is a Fellow of IEEE and a Fellow of the Academy of Engineering Singapore.