Keynote Speakers | 主旨报告

 

Prof. Jiangzhou Wang (王江舟 教授)
University of Kent, U.K.  

- IEEE Communications Society Leonard G. Abraham Prize (2022)
- Fellow of the Royal Academy of Engineering, UK (FREng) (2018)
- Fellow of IEEE (2017)
- 中国工程院外籍院士 (2023)
 

Biosketch: Jiangzhou Wang is a Professor with the University of Kent, U.K. His research interest is in mobile communications. He has published more than 500 papers and five books. He was a recipient of the 2022 IEEE Communications Society Leonard G. Abraham Prize. He was the Technical Program Chair of the 2019 IEEE International Conference on Communications (ICC2019), Shanghai, Executive Chair of the IEEE ICC2015, London, and Technical Program Chair of the IEEE WCNC2013. He is/was the editor of multiple international journals, including IEEE Transactions on Communications from 1998 to 2013. Professor Wang is an International Member of the Chinese Academy of Engineering (CAE), a Fellow of the Royal Academy of Engineering (RAEng), U.K., Fellow of the IEEE, and Fellow of the IET. 

Title of Speech: mmWave Integrated Communications and Sensing 

Abstract: Integrated communications and sensing (ISAC) has become very popular for the next generation mobile communications. This seminar will introduce the concept and challenges of using millimeter wave (mmWave) for ISAC. The latest research results in mmWave ISAC will be presented in conjunction with hybrid beamforming and rate splitting multiple access technologies.  

 

Prof. Yaochu Jin (金耀初 教授)
Westlake University, China (西湖大学) 

欧洲科学院院士, Fellow of IEEE  

Biosketch: Yaochu Jin obtained the BSc., MSc. and PhD degree all in automatic control from the Electrical Engineering Department, Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. from the Institute of Neuroinformatics, Ruhr-University Bochum, Germany in 2001. He is currently a Chair Professor of Artificial Intelligence with the School of Engineering, Westlake University. Before joining Westlake University, he was an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, Bielefeld University, Germany from 2021 to 2023, and a Surrey Distinguished Chair Professor in Computational Intelligence, University of Surrey, Guildford, U.K., from 2010 to 2021. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, and “Changjiang Distinguished Visiting Professor”, Northeastern University, China from 2015 to 2017. He is a Member of Academia Europaea and Fellow of IEEE.  

Title of Speech: Recent Advances in Secure Federated Data-Driven Optimization 

Abstract: Secure and federated data-driven optimization is an emerging research area that aims to protect the data security and privacy used in optimization. This talk starts with an introduction to basic ideas of data-driven optimization and federated privacy-preserving data-driven optimization. To protect the privacy of both offline and online data, we introduce a secure federated data-driven optimization framework based on the Diffie-Hellman protocol, in which a semi-honest client is randomly chosen to solve the acquisition function and determine the next sample point, making sure that newly sampled data is also protected. To reduce the negative impact of the noise added in differential privacy, a utility function is proposed to optimize the noise level that can optimally balance privacy preservation and optimization performance. Finally, a federated multi-tasking data-driven optimization algorithm is presented that shares the hyperparameters of Gaussian processes for knowledge transfer, while protecting the data privacy.  

 

Prof. Zhou Su (苏洲 教授)
Xi'an Jiaotong University, China (西安交通大学)  

Biosketch: Prof. Zhou Su is the Dean of the School of Cyber Science and Engineering at Xi'an Jiaotong University, as well as the Chief Scientist of the National Key Research and Development Program. Additionally, he is recognized as a highly cited scholar in China by Elsevier. His research interests encompass mobile communication networks, IoT security, and cyber-physical systems. He has authored numerous influential articles published in esteemed international journals such as IEEE TIFS, IEEE TDSC, IEEE JSAC, and IEEE/ACM ToN, among others. Moreover, he has been honored with Best Paper Awards at prestigious international conferences including IEEE WCNC 2023, IEEE VTC-FALL 2023, IEEE METACOM 2023, IEEE IWCMC 2022, and IEEE ICC 2020. Furthermore, he holds editorial board positions in distinguished international journals such as the IEEE Internet of Things and the IEEE Open Journal of Computer Society.  

 

INVITED SPEAKERS | 邀请报告 

 

 

Prof. Feng Shu (束锋 教授)
Hainan University, China (海南大学) 

Biosketch: Feng Shu (Member, IEEE) was born in 1973. He received the B.S. degree from Fuyang Teaching College, Fuyang, China, in 1994, the M.S. degree from Xidian University, Xi’an, China, in 1997, and the Ph.D. degree from Southeast University, Nanjing, China, in 2002. From September 2009 to September 2010, he was a Visiting Postdoctoral Fellow with the University of Texas at Dallas, Richardson, TX, USA. From July 2007 to September 2007, he was a Visiting Scholar with the Royal Melbourne Institute of Technology (RMIT University), Melbourne, VIC, Australia. From October 2005 to November 2020, he was with the School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, where he was promoted from an Associate Professor to a Full Professor of supervising Ph.D. students in 2013. Since December 2020, he has been with the School of Information and Communication Engineering, Hainan University, Haikou, China, where he is currently a Professor and a Supervisor of Ph.D. and graduate students. He has authored or coauthored more than 300 in archival journals with more than 180 papers on IEEE journals and 250 SCI-indexed papers. His citations are more than 7100 times. He holds more than 40 Chinese patents and is also a PI or Co-PI for eight national projects. His research interests include machine learning,security,location, and DOA measurement in wireless networks. Prof. Shu is awarded with the Leading-Talent Plan of Hainan Province in 2020, the Fujian Hundred-Talent Plan of Fujian Province in 2018, and the Mingjian Scholar Chair Professor in 2015. He was an IEEE Transactions on Communications Exemplary Reviewer for 2020. He is currently an Editor of IEEE Wireless Communications Letters and is/was a guest Editor for the journals Chinese Journal of Areonautics, IET Communications , IEEE Access, Journal of Electorincs & Information, and Security and Safety. He was an Editor of the IEEE Systems Journal from 2019 to 2021 and IEEE Access from 2016 to 2018. 

Title of Speech: Passive DOA Estimation via massive/Ultra-massive MIMO receive array 

Abstract: Full-digital (FD) Massive/ultra-massive multiple input multiple output (MIMO) based passive detection, direction of arrival (DOA) estimation, Localization will play an irreplaceable role in 6G, and is widely used in techniques such as beamforming, channel estimation, direction modulation, integration of communication and sensing, etc. However, its main disadvantages are high power consumption, circuit costs and computational complexity. To satisfy the demand of future green wireless communication, traditional hybrid analog and digital (HAD) MIMO receive structure is a nature choice. But it exists an issue of the phase ambiguity (PA) in DOA estimation, and has a high latency or low time efficiency. To address this problem, a new heterogeneous HAD (H2AD) is proposed with an intrinsic ability of removing phase ambiguity using only single sampling time-slot , and then a corresponding new framework is developed to remove PA. The proposed framework consists of two steps: 1) form a set of candidate solutions using existing methods like MUSIC or deep learning; 2) infer the class of true solutions from candidate sets using machine learning methods and compute the class mean. To infer the set of true solutions, four cluster-based and two multi-modal learning (MDL) based methods are presented. In addition, to improve DOA estimation accuracy and reduce the clustering complexity, a co-learning-aided MDL framework is proposed to form two enhanced methods. Simularion results confirmed that proposed H2AD structure and framework strikes a good balance among cost, power consumption, complexity, and performance with a low latency.  

 

Prof. Weihua Sheng
Oklahoma State University (OSU), USA.  

Biosketch: Weihua Sheng is a professor at the School of Electrical and Computer Engineering, Oklahoma State University (OSU), USA. He is the Director of the Laboratory for Advanced Sensing, Computation and Control (ASCC Lab, https://ascclab.org) at OSU. Dr. Sheng received his Ph.D degree in Electrical and Computer Engineering from Michigan State University in May 2002. He obtained his M.S and B.S. degrees in Electrical Engineering from Zhejiang University, China in 1997 and 1994, respectively. He has authored more than 240 papers in major journals and international conferences in the area of robotics and automation. Nine of them have won best paper or best student paper awards in major international conferences and journals. His current research interests include social robots, wearable computing and intelligent vehicles. His research has been supported by US National Science Foundation (NSF), Oklahoma Transportation Center (OTC), Center for Advancement of Science & Technology (OCAST), etc. Dr. Sheng is a senior member of IEEE. He served as an Associate Editor for IEEE Transactions on Automation Science and Engineering from 2010 to 2019, and an Associate Editor for IEEE Robotics and Automation Magazine from 2020 to 2023.  

Title of Speech: Robot-assisted Homecare for Aging in Place 

Abstract: With the shortage of qualified home healthcare professionals in many countries, elderly care is becoming a significant societal problem. It is highly desirable to develop new technologies to assist home-bound older adults so they can age independently while enjoying high quality of life, which in the long run will greatly reduce the burden on family members and the healthcare industry. In this talk Dr. Sheng will present his research lab’s effort in developing a robotic assistant called ASCCBot for home healthcare. This talk first introduces the challenges in home healthcare. It then proposes a companion robot-based smart home framework as a solution. Based on this framework, several research problems crucial to robot-assisted home healthcare are presented, including human behavior monitoring, wellbeing assessment, medication management, etc. The talk will be concluded by discussing the future research directions.  

 

Assoc. Prof. Yuanwei Liu (刘元玮 副教授)
Queen Mary University of London, London, UK  

IEEE Fellow, Web of Science Highly Cited Researcher 

Biosketch: Yuanwei Liu is a Senior Lecturer (Associate Professor) in School of Electronic Engineering and Computer Science at Queen Mary University of London (QMUL) , London, U.K. (Aug. 2021-present), where he started as a Lecturer at Sept. 2017. He was a Postdoctoral Research Fellow at King's College London (KCL) , London, U.K. (Sep. 2016- Aug. 2017). He received the Ph.D. degree from QMUL in 2016. He currently serves as the Co-Editor-in-Chief of IEEE ComSoc TC NewsLetter, an Area Editor of IEEE Communications Letters, an Editor of IEEE Transactions on Wireless Communications, IEEE Transactions on Communications, and IEEE Transactions on Network Science and Engineering. He is a Fellow of the IEEE, a Fellow of AAIA, a Web of Science Highly Cited Researcher, an IEEE Communication Society Distinguished Lecturer, and an IEEE Vehicular Technology Society Distinguished Lecturer. Dr. Liu is the recipient of the 2020 [IEEE ComSoc Outstanding Young Researcher Award for the Europe, Middle East and Africa Region], the [2020 Early Achievement Award of the IEEE ComSoc - Signal Processing and Computing for Communications (SPCC) Technical Committee], and the recipient of the [2021 IEEE CTTC Early Achievement Awards].  

Title of Speech: Simultaneously Transmitting And Reflecting Surface (STARS) for 360° Coverage 

Abstract: In this talk, the novel concept of simultaneously transmitting and reflecting surfaces (STARS) will be introduced. First, the STAR basics will be introduced. In particular, the fundamental signal modelling, performance limit characterization, practical operating protocols, joint beamforming design, and channel estimation will be discussed. Then, the multi-functional STARS platform for 6G will be introduced, including sensing STARS, amplifying STARS, and cache STARS. Furthermore, several promising case studies of employing STARS will be put forward, including STARS-NOMA, STARS-THz, spatial analysis for STARS, and federated learning with STARS. Finally, standardisation and commercial progress of STARS as well as research opportunities and problem will be discussed.  

 

Dr. Jingqing Wang (王婧青 博士)
Xidian University, China (西安电子科技大学)  

Biosketch: Jingqing Wang received the B.S. degree from Northwestern Polytechnical University, Xi'an, China, in Electronics and Information Engineering and the Ph.D. degree from Texas A&M University, College Station, TX, USA, in Computer Engineering in 2022. She is currently a Jingying assistant professor with Xidian University. She has published more than 60 international journal and conference papers in IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, IEEE magazines, IEEE TRANSACTIONS, IEEE INFOCOM, GLOBECOM, WCNC, and ICC. She won the Best Paper Award from the IEEE GLOBECOM in 2020 and 2014, respectively. Her current research interests focus on next generation mobile wireless network technologies, statistical QoS provisioning, 6G mURLLC, information-theoretic analyses of FBC, emerging machine learning techniques.  

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