Prof. Marco Di Renzo

Paris-Saclay University, CNRS, France

Reconfigurable Intelligent Surfaces for Wireless Communications

Reconfigurable intelligent surfaces (RISs) have recently emerged as the new wireless communication research frontier with the goal of realizing metamaterial-coated smart and reconfigurable radio propagation environments through passive and tunable signal transformations. Featured by orders of magnitude lower hardware and energy cost than traditional active antenna-arrays and yet superior performance, RISs are the new driving technology for future wireless networks, especially for enabling them to migrate to higher frequency bands. RISs have the inherent potential of transforming the current wireless network with active nodes solely into a new hybrid network comprising active and passive components co-working in an intelligent way, so as to achieve a sustainable capacity growth with a low and affordable cost and power consumption. Therefore, RISs have the potential to change how wireless networks are currently designed, usher in that hoped-for wireless future, and are regarded as an enabling technology for realizing the emerging concept of metamaterial-assisted smart (programmable) radio environments (SREs). RIS-assisted SREs are a multidisciplinary research endeavor but are not well-understood. In this talk, I will introduce the state of the art, the research challenges, and some recent research results at the crossroad of wireless communications, electromagnetics, physics, and metamaterials.

Short Biography

Marco Di Renzo 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, France, in 2013. Since 2010, he has been with the French National Center for Scientific Research (CNRS), where he is a CNRS Research Director (CNRS Professor) in the Laboratory of Signals and Systems (L2S) of Paris-Saclay University – CNRS and Centrale Supelec, Paris, France. In Paris-Saclay University, he serves as the Coordinator of the Communications and Networks Research Area of the Laboratory of Excellence DigiCosme, and as a member of the Admission and Evaluation Committee of the Ph.D. School on Information and Communication Technologies. Currently, he serves as the Editor-in-Chief of IEEE Communications Letters, and is a Distinguished Speaker of the IEEE Vehicular Technology Society. In 2017-2020, he was a Distinguished Lecturer of the IEEE Vehicular Technology Society and IEEE Communications Society. Also, he served as an Editor and the Associate Editor-in-Chief of IEEE Communications Letters, and as an Editor of IEEE Transactions on Communications and IEEE Transactions on Wireless Communications. Currently, he serves as the Founding Chair of the Special Interest Group on Reconfigurable Intelligent Surfaces of the Wireless Technical Committee of the IEEE Communications Society, and is the Founding Lead Editor of the IEEE Communications Society Best Readings in Reconfigurable Intelligent Surfaces. He is a Highly Cited Researcher (Clarivate Analytics, 2019), a World’s Top 2% Scientist from Stanford University (2020), a Fellow of the IEEE (2020), and a Fellow of the IET (2020). He has received several individual distinctions and research awards, which include the IEEE Communications Society Best Young Researcher Award for Europe, Middle East and Africa, the Royal Academy of Engineering Distinguished Visiting Fellowship, the IEEE Jack Neubauer Memorial Best System Paper Award, the IEEE Communications Society Young Professional in Academia Award, the SEE-IEEE Alain Glavieux Award, and a 2019 IEEE ICC Best Paper Award. In 2019, he was a recipient of a Nokia Foundation Visiting Professorship for conducting research on metamaterial-assisted wireless communications at Aalto University, Finland.

Prof. Emil Bjornsson

Royal Institute of Technology, Sweden

Prof. Luca Sanguinetti

University of Pisa, Italy

Dr. Özlem Tuğfe Demir

Royal Institute of Technology, Sweden

User-Centric Cell-Free Massive MIMO: From Foundations to Scalable Implementation​

As the first release of 5G has recently been finalized and commercial networks have been launched, it is time to look for new forward-looking research directions that have the potential to revolutionize the future of wireless. Just as the seminal papers on Massive MIMO were published 10 years ago, this is likely the time when the key technology components for 6G will be identified. Hence, this is the right time for researchers to catch onto new research directions and potentially find the next big thing.
In this tutorial, we will first go through the foundations of the original Cell-Free Massive MIMO, which is a recently proposed alternative infrastructure to overcome the key weaknesses of cellular systems. The first papers in this area are just a few years old and there are still many open problems. One important issue with the original form of Cell-Free Massive MIMO is scalability, which is one of the key topics that we will cover in this tutorial as well as possible solutions such as user-centric dynamic cooperation clustering. We will describe different methods for signal processing and spatial resource allocation including optimized techniques and scalable heuristics solutions. After providing some implementation constraints, we will discuss open problems related to this research direction.
The presentation will be based on our recently published textbook on the topic, “Foundations of User-Centric Cell-Free Massive MIMO”, which summarizes the state-of-the-art in the field and is the first book of its kind on this emerging topic. We  are also planning to provide a free PDF of this book to the conference attendees.

Short Biography

Emil Björnson (S’07-M’12-SM’17) is currently a Visiting professor at KTH Royal Institute of Technology, Sweden, and Associate Professor at Linköping University, Sweden. He has authored the textbooks Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013), Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency (2017), and Foundations of User-Centric Cell-Free Massive MIMO (2021). He is dedicated to reproducible research and has made a large amount of simulation code publicly available. He performs research on MIMO communications, radio resource allocation, machine learning for communications, and energy efficiency. Since 2017, he has been on the Editorial Board of the IEEE Transactions on Communications and the IEEE Transactions on Green Communications and Networking since 2016. He has received the 2014 Outstanding Young Researcher Award from IEEE ComSoc EMEA, the 2015 Ingvar Carlsson Award, the 2016 Best Ph.D. Award from EURASIP, the 2018 IEEE Marconi Prize Paper Award in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society Fred W. Ellersick Prize, and the 2019 IEEE Signal Processing Magazine Best Column Award. He also co-authored papers that received Best Paper Awards at the conferences, including WCSP 2009, the IEEE CAMSAP 2011, the IEEE WCNC 2014, the IEEE ICC 2015, WCSP 2017, and the IEEE SAM 2014.

Luca Sanguinetti (SM’15) is currently an Associate Professor with the Dipartimento di Ingegneria dell’Informazione, University of Pisa. He has coauthored the textbooks Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency (2017) and Foundations of User-Centric Cell-Free Massive MIMO (2021). His expertise and general interests span the areas of communications and signal processing. Dr. Sanguinetti was a recipient of the 2018 Marconi Prize Paper Award in Wireless Communications and coauthored an article that received the Young Best Paper Award from the ComSoc/VTS Italy Section. He was the recipient of the FP7 Marie Curie IEF 2013 “Dense deployments for green cellular networks”. He was also a co-recipient of the two best conference paper awards: IEEE WCNC 2013 and IEEE WCNC 2014. He served as an Associate Editor for the IEEE Transactions on Wireless Communications and the IEEE Journal on Selected Areas of Communications (series on Green Communications and Networking) and as a Lead Guest Editor for the IEEE Journal on Selected Areas of Communications Special Issue on “Game Theory for Networks”. He is currently serving as an Associate Editor for the IEEE Signal Processing Letters, the IEEE Transactions on Communications. He is also a member of the Executive Editorial Committee of the IEEE Transactions on Wireless Communications.

Özlem Tuğfe Demir received the B.S., M.S., and Ph.D. degrees in Electrical and Electronics Engineering from Middle East Technical University, Ankara, Turkey, in 2012, 2014, and 2018, respectively. She was a Postdoctoral Researcher with Linköping University, Sweden in 2019-2020. She is currently a Postdoc with KTH Royal Institute of Technology, Sweden. She has coauthored the textbook Foundations of User-Centric Cell-Free Massive MIMO (2021). Her research interests focus on signal processing and optimization in wireless communications, massive MIMO, beyond 5G multiple antenna technologies, deep learning, and green communications. She is a recipient of the IEEE SIU 2015 Conference Student Best Paper Award, the Best Thesis Award for M.S. Program and Graduate Courses Performance Award at the Middle East Technical University. 

Prof. Guilherme de Alencar Barreto

Federal University of Ceará, Brazil

Adaptive Kernel Filtering and Applications in the Modeling of
Dynamic Systems

In this tutorial we will cover the kernel versions of adaptive linear filters, such as the famous LMS and RLS filters. The kernel-based formulation makes such filters capable of handling signals generated by non-linear dynamic systems. However, it also brings considerable disadvantages, such as a considerable increase in the complexity of the filter due to the need to store all the observed samples to build the kernel matrix at each instant of time. In order to mitigate this effect, different sparsifying methods are available in the literature, in which a dictionary with a small amount of relevant samples is built over time. In this tutorial, we will discuss variants of the LMS kernel (KLMS) and RLS kernel (KRLS) filters, with an emphasis on the latter, as well as different dictionary building techniques and recent contributions in this area made in our research group. Other important concepts to be addressed in the tutorial: currentropy, robustness to outliers and tracking ability of KRLS filters. Applications in signal processing, prediction of time series and identification of dynamic systems will be presented and discussed.

Short Biography

Guilherme de Alencar Barreto has a bachelor degree in Electrical Engineering from the Federal University of Ceará, a master degree in Electrical Engineering from the School of Engineering of São Carlos/USP (1998) and a doctorate in Electrical Engineering, also from the School of Engineering of São Carlos/USP (2003), with a period of sandwich doctorate at the University of Bielefeld (Germany). He is an associate professor in the Department of Teleinformatics Engineering, Federal University of Ceará. His main research area is Computational Intelligence and Machine Learning with applications in robotics, identification and control of dynamic systems, and pattern recognition. He is a member of the Brazilian Society of Computational Intelligence (SBIC), having been its president in the 2015-2017 biennium, and of the Brazilian Society of Automation (SBA). He is editor-in-chief of Learning & Nonlinear Models (L&NLM) journal, published by SBIC, since 2009. He is also associate editor of Applied Intelligence (Springer), International Journal of Machine Learning and Cybernetics (Springer), International Journal of Innovative Computing and Applications (Inderscience) and Frontiers. He is currently the coordinator of the Graduate Program in Teleinformatics Engineering-PPGETI (CAPES 6), Center of Technology, Federal University of Ceará.