Speakers
Prof. Haijun Zhang
IEEE Fellow
University of Science and Technology Beijing, China
Title: Intelligent Resource Optimization in 6G
Abstract:
This talk will identify and discuss technical challenges and recent results related to 6G mobile resource optimization. The talk is mainly divided into four parts. The first part will introduce 6G mobile networks, discuss about the 6G mobile networks architecture, and provide some main technical challenges in 6G mobile networks. The second part will focus on the issue of resource management in 6G networks and provide different recent research findings that help to develop engineering insights. The third part will address the machine learning and deep learning method based future 6G networks and address some key research problems. The last part will summarize by providing a future outlook of intelligent resource optimization in 6G mobile networks.
Biography:
Haijun Zhang ( IEEE Fellow ) is currently a full professor, deputy Dean and doctoral supervisor of the School of Computer and Communication Engineering, University of Science and Technology Beijing, China. He was a Postdoctoral Research Fellow in Department of Electrical and Computer Engineering, the University of British Columbia (UBC), Canada.
His research interests include 6G Mobile Communications, Artificial Intelligence and Wireless Networks, B5G+ Industrial Internet, Machine Learning and Big Data. He has Published more than 100 SCI papers in IEEE authoritative journals Area 1 / Area 2, and authorized a number of invention patents.
Prof. Haijun Zhang is/was the editorial board member of IEEE TCOM, IEEE TNSE and other IEEE journals, Vice Chairman of IEEE Green Communication and Computing Technical Committee, and President of the 6th International Conference on Game Theory and Networks. He received the IEEE CSIM Technical Committee Best Journal Paper Award in 2018, IEEE ComSoc Young Author Best Paper Award in 2017, IEEE ComSoc Asia-Pacific Best Young Researcher Award in 2019, IEEE ComSoc Distinguished Lecturer.
Prof. Guan Gui
IEEE Fellow, IET Fellow
AAIA Fellow, IEEE VTS Distinguished Lecturer
Nanjing University of Posts and Telecommunications, China
Title: Intelligent Signal Sensing and Recognition Techniques Towards Physical-Layer 6G Security
Abstract:
The dawn of 6G wireless communication introduces a transformative era characterized by pervasive sensing and advanced intelligent identification, essential for ensuring physical security. This keynote speech highlights the integration of Artificial Intelligence (AI) and Deep Learning (DL) as pivotal in addressing the dynamic and complex challenges of 6G networks. We emphasize the role of AI in revolutionizing signal sensing and recognition. Our discussion centers on the application of these neural networks in enhancing signal detection, classification, and Specific Emitter Identification (SEI). By leveraging gradient-based optimization techniques, we demonstrate how ANNs can improve model and algorithm parameterization, leading to a data-driven approach that surpasses traditional rule based systems. This advancement is crucial in the physical layer of wireless communications, where intelligent signal recognition plays a key role in maintaining security and efficiency. We also explore the challenges faced by conventional model-based methods in the evolving landscape of 6G communication systems, which are marked by complex interference and uncertain channel conditions. DL emerges as a solution, offering innovative strategies for redesigning baseband module functionalities, including coding/decoding and detection processes. In conclusion, this keynote underscores the significance of integrating intelligent signal sensing and recognition with DL technologies in 6G networks. This approach not only enhances physical security but also paves the way for a more robust, efficient, and intelligent wireless communication ecosystem, capable of meeting the security demands of the future.
Biography:
Guan Gui received the Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China, in 2012. From 2009 to 2014, he joined Tohoku University as a Research Assistant and a Post-Doctoral Research Fellow. From 2014 to 2015, he was an Assistant Professor with Akita Prefectural University, Akita, Japan. Since 2015, he has been a Professor with the Nanjing University of Posts and Telecommunications, Nanjing, China. He has published more than 200 IEEE journals/conference papers. His recent research interests include intelligence sensing and recognition, intelligent signal processing, and physical layer security. Dr. Gui contributions to intelligent signal analysis and wireless resource optimization have earned him the title of fellow of the IEEE, IET, and AAIA. He was a recipient of several Best Paper Awards, such as ICC 2017, ICC 2014, and VTC 2014-Spring. He received the IEEE Communications Society Heinrich Hertz Award in 2021, top 2% scientists of the world by Stanford University from 2021 to 2023, the Clarivate Analytics Highly Cited Researcher in Cross-Field from 2021 to 2023, the Highly Cited Chinese Researchers by Elsevier from 2020 to 2023, a member and Global Activities Contributions Award in 2018, the Top Editor Award of IEEE Transactions on Vehicular Technology in 2019, the Outstanding Journal Service Award of KSII Transactions on Internet and Information System in 2020, the Exemplary Reviewer Award of IEEE Communications Letters in 2017, the 2012 Japan Society for Promotion of Science (JSPS) Postdoctoral Fellowships for Foreign Researchers, and the 2018 Japan Society for Promotion of Science (JSPS) International Fellowships for Overseas Researchers. He was also selected as the Jiangsu Specially-Appointed Professor in 2016, the Jiangsu High-Level Innovation and Entrepreneurial Talent in 2016, and the Jiangsu Six Top Talent in 2018.
Prof. Antonio De Maio
IEEE Fellow
University of Naples Federico II, Italy
Title: Rule-Based Scheduling for MPARs performing Sensing and Communications
Abstract:
A Multifunction phased array radar (MPAR) is capable of performing sensing and communication by functionally grouping a phased array into tailored sub-apertures, each dedicated to a distinct task. Because of limited available resources, such as bandwidth, power aperture product, and time, it is important to properly allocate them to each sub-aperture. This lecture examines a rule-based task scheduling algorithm wherein communication (COM) looks are employed to fill the vacant intraslot time left by the radar tasks that are previously allocated, namely, volume and cued search, update and confirmation tracking. The allocation of the looks is performed to each time slot according to the task priority, but some of them (i.e., volume/cued/COM) are parallelized when the available bandwidth and power-aperture product (PAP) allow for it. Simulations conducted in some relevant scenarios demonstrate the validity of the proposed allocation strategy in terms of bandwidth utilization and time occupancy.
Biography:
Antonio De Maio (S'01-A'02-M'03-SM'07-F'13) was born in Sorrento, Italy, on June 20, 1974. He received the Dr.Eng. degree (with honors) and the Ph.D. degree in information engineering, both from the University of Naples Federico II, Naples, Italy, in 1998 and 2002, respectively. Currently, he is a Professor with the University of Naples Federico II. His research interest lies in the field of statistical signal processing, with emphasis on radar detection and optimization theory applied to radar signal processing. Dr. De Maio is a Fellow member of IEEE and the recipient of the 2010 IEEE Fred Nathanson Memorial Award as the young (less than 40 years of age) AESS Radar Engineer 2010 whose performance is particularly noteworthy as evidenced by contributions to the radar art over a period of several years, with the following citation for "robust CFAR detection, knowledge-based radar signal processing, and waveform design and diversity". Dr. De Maio is the recipient of the 2024 IEEE AESS Warren White Award, to recognize a radar engineer for outstanding achievements due to a major technical advance (or series of advances) in the art of radar engineering, with the following citation “for contributions to radar signal processing techniques for target detection, waveform design and electronic protection”.
Prof. Ram Bilas Pachori
IETE Fellow, IEI Fellow, IET Fellow
Indian Institute of Technology Indore, India
Title: Multi-channel iterative filtering and applications
Abstract:
In the last one or two decades, adaptive signal decomposition techniques have gained popularity for their broad applicability to almost all fields of science and technology. Empirical mode decomposition has been proposed to decompose the signal into amplitude-frequency modulated components (basis functions). Several methods have been proposed, followed by empirical mode decomposition for adaptive decomposition and to obtain improved signal representation. Empirical wavelet transform (EWT), Fourier-Bessel series expansion-based EWT (FBSE-EWT), iterative filtering, variational mode decomposition are a few popular techniques among adaptive decomposition techniques. Recent advancements in sensor technology make it easier to acquire signals from multiple sources simultaneously, which demands multivariate/multi-channel signal decomposition methods. The univariate iterative filtering has been extended for processing multichannel signals, which will be discussed in this talk. Also, applications of multivariate iterative filtering and machine learning in brain-computer interface and schizophrenia detection from multichannel electroencephalogram (EEG) signals will be presented. The obtained results show the effectiveness of the discussed multivariate/multi-channel adaptive signal decomposition techniques.
Biography：
Ram Bilas Pachori received the B.E. degree with honours in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India, in 2001, the M.Tech. and Ph.D. degrees in Electrical Engineering from IIT Kanpur, India, in 2003 and 2008, respectively. Before joining the IIT Indore, India, he was a Post-Doctoral Fellow at the Charles Delaunay Institute, University of Technology of Troyes, France (2007-2008) and an Assistant Professor at the Communication Research Center, International Institute of Information Technology, Hyderabad, India (2008-2009). He was an Assistant Professor (2009-2013) and an Associate Professor (2013-2017) at the Department of Electrical Engineering, IIT Indore, where he has been a Professor since 2017. He is also associated with the Center for Advanced Electronics, IIT Indore. He was a Visiting Professor at the Department of Computer Engineering, Modeling, Electronics and Systems Engineering, University of Calabria, Rende, Italy, in July 2023; Faculty of Information & Communication Technology, University of Malta, Malta, from June 2023 to July 2023; Neural Dynamics of Visual Cognition Lab, Free University of Berlin, Germany, from July 2022 to September 2022; School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Malaysia, from 2018 to 2019. Previously, he was a Visiting Scholar at the Intelligent Systems Research Center, Ulster University, Londonderry, UK, in December 2014. His research interests include signal and image processing, biomedical signal processing, non-stationary signal processing, speech signal processing, brain-computer interface, machine learning, and artificial intelligence and the internet of things in healthcare. He was an Associate Editor of IEEE Transactions on Neural Systems and Rehabilitation Engineering (2021-2024). Currently, he is an Associate Editor of Electronics Letters, IEEE Open Journal of Engineering in Medicine and Biology, Computers and Electrical Engineering, and Biomedical Signal Processing and Control, and an Editor of IETE Technical Review journal. He is a Fellow of IETE, IEI, and IET. He has authored the textbook titled “Time-Frequency Analysis Techniques and their Applications” (CRC Press, 2023). He has supervised 19 Ph.D. theses (13 Ph.D. theses under progress). He has 339 publications, which include journal articles (213), conference papers (89), books (10), and book chapters (27). He has also eight patents, including one Australian patent (granted) and seven Indian patents (published). His publications have been cited approximately 17000 times with h-index of 71 according to Google Scholar.
Prof. Haiquan Zhao
IEEE Senior Member
Southwest Jiaotong University, China
Title: Graph signal processing based on information theoretic learning
Abstract:
In real life, many data are recorded in irregular and interactive structures, such as social networks and traffic networks. Such complex data structures are difficult to manipulate using traditional signal processing tools, and graph provide a way to solve the problem of modeling these data and the complex structures between them.This topic shares our recent research findings.
Biography:
Zhao Haiquan, Doctor of Engineering, professor, doctoral supervisor, senior member of IEEE and Chinese Institute of Electronics, academic and technical leader in Sichuan Province, outstanding expert with outstanding contributions in Sichuan Province, winner of Sichuan Outstanding Youth Fund and core member of Sichuan Youth Science and Technology Innovation Team, Won the First Prize of Natural Science of CAA of Chinese Society of Automation in 2019 (ranked 3rd), the second Prize of Natural Science of CAA of Chinese Society of Automation in 2018 (ranked 1st), the second Prize of Science and Technology Progress of the Ministry of Education (ranked 3rd), and the 12th Jeme Tianyou Railway Science and Technology Award - Youth Award. The 9th Jeme Tianyou Science and Technology Award of Ministry of Railways - Jeme Tianyou Special Award of Southwest Jiaotong University, Tang Lixin Outstanding Scholar Award; Executive Director of Provincial Science and Youth Federation, member of Sichuan Youth Federation, Executive director of Chengdu Knowledge Association. Serves on the editorial board of international journals such as Signal Processing.
