Prof. Rui Zhang
Title: Towards Smart and Reconfigurable Radio Environment: Intelligent Reflecting Surface Aided Wireless Communication
Bio:
Rui Zhang (IEEE Fellow) received the Ph.D. degree from Stanford University in electrical engineering in 2007. He is now a Professor in the Department of Electrical and Computer Engineering, National University of Singapore. His current research interests include wireless power transfer, UAV communication, and reconfigurable MIMO. He has published over 400 papers, which have been cited more than 40,000 times with h-index over 100. He has been listed as a Highly Cited Researcher by Thomson Reuters/Clarivate Analytics since 2015. His works have received several IEEE awards, including the IEEE Marconi Prize Paper Award in Wireless Communications (twice), the IEEE Communications Society Heinrich Hertz Prize Paper Award (twice), the IEEE Signal Processing Society Best Paper Award, Young Author Best Paper Award and Donald G. Fink Overview Paper Award, etc. He has served as an Editor for several IEEE journals, including TWC, TCOM, JSAC, TSP, TGCN, etc., and as TPC co-chair or organizing committee member for over 30 international conferences. He is an IEEE Distinguished Lecturer of IEEE Communications Society and IEEE Signal Processing Society.
Abstract:
Intelligent Reflecting Surface (IRS) has recently emerged as the new wireless communication research frontier with the goal of achieving smart and reconfigurable radio propagation environment via passive and tunable signal reflections. Featured by orders-of-magnitude lower hardware and energy cost than traditional active arrays and yet superior beamforming performance as well as other new functionalities, IRS is expected to be a new driving technology for future B5G/6G wireless networks, especially for enabling them to migrate to higher frequency bands (mmWave/THz). Moreover, IRS will fundamentally transform today’s wireless network with active nodes solely to a new IRS-aided hybrid network comprising both active and passive components co-working in an intelligent way, so as to achieve a sustainable capacity growth with low and affordable cost in the future. In this talk, we will provide an overview of IRS, including its motivations, promising applications in wireless network, communication basics, new design challenges, and their state-of-the-art solutions. Important directions worthy of further investigation such as machine learning empowered IRS will also be discussed.
Prof. Wei Xiang
Title: Artificial Intelligent Internet of Underwater Things: Opportunities, Applications and Challenges
Bio:
Wei Xiang is Cisco Professorial Chair of AI and Internet of Things and Director of Cisco-La Trobe Centre for AI and IoT at La Trobe University. Previously, he was Foundation Chair and Head of Discipline of Internet of Things Engineering at James Cook University, Cairns, Australia. Due to his instrumental leadership in establishing Australia’s first accredited Internet of Things Engineering degree program, he was inducted into Pearcy Foundation’s Hall of Fame in October 2018. He is an elected Fellow of the IET in UK and Engineers Australia. He received the TNQ Innovation Award in 2016, and Pearcey Entrepreneurship Award in 2017, and Engineers Australia Cairns Engineer of the Year in 2017. He was a co-recipient of four Best Paper Awards at WiSATS’2019, WCSP’2015, IEEE WCNC’2011, and ICWMC’2009. He has been awarded several prestigious fellowship titles. He was named a Queensland International Fellow (2010-2011) by the Queensland Government of Australia, an Endeavour Research Fellow (2012-2013) by the Commonwealth Government of Australia, a Smart Futures Fellow (2012-2015) by the Queensland Government of Australia, and a JSPS Invitational Fellow jointly by the Australian Academy of Science and Japanese Society for Promotion of Science (2014-2015). He is the Vice Chair of the IEEE Northern Australia Section. He holds a US patent. He was an Editor for IEEE Communications Letters (2015-2017), and is an Associate Editor for IEEE Access and Springer’s Telecommunications Systems. He has published over 280 peer-reviewed papers including 3 academic books and 180 journal articles. He has severed in a large number of international conferences in the capacity of General Co-Chair, TPC Co-Chair, Symposium Chair, etc. His research interest includes the Internet of Things, wireless communications, machine learning for IoT data analytics, and computer vision.
Abstract:
As IoT devices will generate vast amounts of data, then AI will be functionally necessary to deal with these huge volumes if one is to make sense of the data. In this talk, we will advocate that AI+IoT (AIoT) is a data-driven ecosystem, which consists of three layers, namely data acquisition, data communications, and data analytics. Traditional methods of analysing structured data and creating action are not designed to efficiently process the vast amounts of real-time data that stream from IoT devices. This is where AI-empowered analysis and response becomes critical for extracting optimal value from that data. We will use marine IoT as an exemplary use case to illustrate how novel AIoT technology will be able to help protect one of the nature’s greatest treasures – the Great Barrier Reef. We will show through joining forces with smart AI network models, IoT technology can significantly reduce IoT sensing costs for the marine environment, while improving the reliability of the sensory data.