Schedule

Biography:

Professor Xuemin (Sherman) Shen is currently a University Professor with the Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada. His research interests include network resource management, wireless network security, social networks, 5G and beyond, and vehicular ad hoc and sensor networks. He was a Distinguished Lecturer of the IEEE Vehicular Technology Society and Communications Society. Professor Shen received the R.A. Fessenden Award in 2019 from IEEE, Canada, Award of Merit from the Federation of Chinese Canadian Professionals (Ontario) in 2019, the James Evans Avant Garde Award in 2018 from the EEE Vehicular Technology Society, the Joseph LoCicero Award in 2015. the Education Award in 2017 from the IEEE Communications Society and the Technical Recognition Award from Wireless Communications Technical Committee in 2019 and AHSN Technical Committee in 2013. He was the Technical Program Committee Chair or Co-Chair for IEEE Globecom'16, IEEE Infocom14, IEEE VTC10 Fall, IEEE Globecom'07, and the Chair for the IEEE Communications Society Technical Committee on Wireless Communications. Professor Shen is the IEEE Communications Society President. He was the Vice President for Technical and Educational Activities, the Vice President for Publications, the Chair of the Distinguished Lecturer Selection Committee, a Member of IEEE ComSoc Fellow Selection Committee. He was the Editor-in-Chief of the IEEE INTERNET OF THINGS JOURNAL, IEEE NETWORK and IET Communications. Professor Shen is a Fellow of the IEEE, Royal Society of Canada, Canadian Academy of Engineering and Engineering Institute of Canada, and a Foreign Member of Chinese Academy of Engineering.

Title: AI-Assisted Network Management: Network Slicing and Digital Twin

Abstract:

With the deployment of 5G, the research focus has been shifted to 6G. To support disruptive new applications in highly dynamic networks, it is envisioned that 6G requires agile network architectures for adapting to network dynamics, intelligent network management for solving complex network management problems imposed by heterogeneous networks, and customized service provision to satisfy diverse service requirements. In this talk, we try to initiate new ideas and solutions towards flexible and intelligent network management and adaptive and multi-grained resource allocation. First, I will give a brief introduction to network slicing and digital twin, and how network slicing and digital twin can support network management by incorporating artificial intelligence. Then, I will talk about our two recent works: Radio Resource Reservation with Network Slicing, and Digital Twin-Assisted Network Management for Multi-Tier Computing. I will provide conclusion remark at the end.

Biography:

Marco Di Renzo (Fellow, IEEE) received the Laurea (cum laude) and Ph.D. degrees in electrical engineering from the University of L’Aquila, Italy, in 2003 and 2007, respectively, and the Habilitation à Diriger des Recherches (Doctor of Science) degree from University Paris-Sud (currently Paris-Saclay University), France, in 2013. Currently, he is a CNRS Research Director (Professor) and the Head of the Intelligent Physical Communications group in the Laboratory of Signals and Systems (L2S) of Paris-Saclay University – CNRS and CentraleSupelec, Paris, France. At Paris-Saclay University, he serves as the Coordinator of the Communications and Networks Research Area of the Laboratory of Excellence DigiCosme, as a Member of the Admission and Evaluation Committee of the Ph.D. School on Information and Communication Technologies, and as a Member of the Evaluation Committee of the Graduate School in Computer Science. He is a Founding Member and the Academic Vice Chair of the Industry Specification Group (ISG) on Reconfigurable Intelligent Surfaces (RIS) within the European Telecommunications Standards Institute (ETSI), where he serves as the Rapporteur for the work item on communication models, channel models, and evaluation methodologies. He is a Fellow of the IEEE, IET, and AAIA; an Ordinary Member of the European Academy of Sciences and Arts, an Ordinary Member of the Academia Europaea; and a Highly Cited Researcher. Also, he is a Fulbright Fellow at City University of New York, USA, and was a Nokia Foundation Visiting Professor and a Royal Academy of Engineering Distinguished Visiting Fellow. His recent research awards include the 2021 EURASIP Best Paper Award, the 2022 IEEE COMSOC Outstanding Paper Award, the 2022 Michel Monpetit Prize conferred by the French Academy of Sciences, the 2023 EURASIP Best Paper Award, the 2023 IEEE COMSOC Fred W. Ellersick Prize, and the 2023 IEEE COMSOC Heinrich Hertz Award. He serves as the Editor-in-Chief of IEEE Communications Letters.

Title: Intelligent Surfaces for Wireless Communications: Living at the Interface of Electromagnetic and Communication Theories

Abstract:

In wireless communications, the term intelligent surface is referred to a planar metamaterial structure that is capable of generating an arbitrary current density distribution, so as to ensure the highest flexibility in generating a specified electromagnetic field and in shaping the propagation of the electromagnetic waves in large-scale networks. This presentation is aimed to report the latest research advances on analytical modeling, evaluating the ultimate performance limits, and optimizing intelligent surfaces for application to wireless communications, with focus on the synergies between electromagnetic and communication theories.

Biography:

Octavia A. Dobre (Fellow, IEEE) is a Professor and Canada Research Chair Tier 1 at Memorial University, Canada. She was a Visiting Professor with Massachusetts Institute of Technology, USA and Université de Bretagne Occidentale, France. Previously, she was with New Jersey Institute of Technology, Stevens Institute of Technology and “Politehnica” University of Bucharest. Her research interests encompass wireless communication and networking technologies, as well as optical and underwater communications. She has (co-)authored over 450 refereed papers in these areas. Dr. Dobre serves as the Director of Journals of the Communications Society. She was the inaugural Editor-in-Chief (EiC) of the IEEE Open Journal of the Communications Society and the EiC of the IEEE Communications Letters. She also served as General Chair, Technical Program Co-Chair, Tutorial Co-Chair, and Technical Co-Chair of symposia at numerous conferences. Dr. Dobre was a Fulbright Scholar, Royal Society Scholar, and Distinguished Lecturer of the IEEE Communications Society. She obtained Best Paper Awards at various conferences, including IEEE ICC, IEEE Globecom, IEEE WCNC, and IEEE PIMRC. Dr. Dobre is an elected member of the European Academy of Sciences and Arts, a Fellow of the Engineering Institute of Canada, and a Fellow of the Canadian Academy of Engineering.

Title: Full Duplex Communications for the Next Generation Wireless Networks

Abstract:

With work on the 3GPP Release 18 ongoing, 5G-Advanced is on its way. An unprecedented proliferation of new Internet-of-everything services is continuing, such as extended reality, aerial vehicles, automation of industry, and connected autonomous systems, leading to the digital transformation of the society. In the last few years, the research community has started to look toward the next generation (6G) of wireless networks, which aims to bring us closer to the fully connected, intelligent digital world of the future. This talk will briefly discuss the vision for 6G wireless networks, and then focus on the full duplex technology which theoretically doubles the sum rate and enables reduced latency. In particular, different machine learning-based methods will be presented to tackle the critical self-interference problem in full duplex transceivers. The talk will conclude with directions for future investigation in the next generation wireless networks.

Biography:

Olgica Milenkovic is the Franklin W. Woeltge professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign (UIUC), and Research Professor at the Coordinated Science Laboratory. She obtained her Master's Degree in Mathematics in 2001 and PhD in Electrical Engineering in 2002, both from the University of Michigan, Ann Arbor. Prof. Milenkovic heads a group focused on addressing unique interdisciplinary research challenges spanning the areas of algorithm design and computing, bioinformatics, coding theory, machine learning and signal processing. Her scholarly contributions have been recognized by multiple awards, including the NSF Faculty Early Career Development (CAREER) Award, the DARPA Young Faculty Award, the Dean’s Excellence in Research Award, and several best paper awards. In 2013, she was elected a UIUC Center for Advanced Study Associate and Willett Scholar while in 2015 she was elected Distinguished Lecturer of the Information Theory Society. In 2018 she became an IEEE Fellow. She has served as Associate Editor of the IEEE Transactions of Communications, the IEEE Transactions on Signal Processing, the IEEE Transactions on Information Theory and the IEEE Transactions on Molecular, Biological and Multi-Scale Communications. In 2009, she was the Guest Editor in Chief of a special issue of the IEEE Transactions on Information Theory on Molecular Biology and Neuroscience.

Title: Coding solutions for polymer-based data storage

Abstract:

Molecular storage systems are ultradense information recorders that use DNA, proteins or polymers as storage media. By now, many efficient coding solutions are known for DNA-based data storage platforms, but the unique challenges associated with reading information from other molecular media have not been properly addressed. To mitigate this problem, we introduce unique reconstruction and error-control codes for synthetic polymer-based data-storage. In polymer-based storage systems, binary information strings are represented by chains comprising two molecules of substantially different masses. Writing amounts to stitching the “molecular bits” together while reading is performed via tandem mass spectrometry. Roughly speaking, mass spectrometers report noisy measurements of masses of collections of substrings of the binary string. Unique reconstruction codes ensure that each coded string can be uniquely recovered given the masses of all of its substrings. Mass spectrometry error-correction codes, on the other hand, offer both unique reconstruction properties as well as resilience to a fixed number of mass errors. We describe how to construct such codes using techniques developed for solving the Turnpike problem, Catalan paths and evaluation codes. We also consider the same problems applied to mixtures of strings, which is a more practically relevant setting. In this case, we use Knuth-balancing type of arguments with new constructions for binary and nonbinary B_h sequences. Our results show that reconstruction and limited mass error-correction constraints incur negligible coding redundancy, while mixing strings results in coding rates that scale inversely proportional with the number of strings in the mixture.