Wireless 2.0: Towards a Smart Radio Environment Empowered by Reconﬁgurable Intelligent Metasurfaces and Artiﬁcial Intelligence
5G wireless networks are designed based on the fundamental postulate that only the end-points of the communication links, i.e., the transmitters and the receivers, can be optimized for improving the network performance. The propagation environment that lies in between them is, on the other hand, out of control of the communication engineers. In other words, while the transmitters and receivers can be controlled and optimized, the environmental objects (e.g., walls, buildings, furniture, ceilings, floors, etc.) that constitute the wireless environment cannot be customized based on the network conditions.
Today, therefore, communication engineers are used to model the radio environment as an entity that the transmitters and receivers need to adapt to, in order to either counteract or to leverage the propagation channel. Typical approaches include multiple antennas, sophisticated encoding/decoding schemes, and advanced communication protocols. These approaches have allowed wireless networks to increase the capacity-per-unit-of-energy by a 1000-fold factor in the last 20 years. However, contemporary wireless communication systems remain extremely inefficient due to the constraints imposed by the radio environment per se. A typical base station, for example, transmits radio waves of the order of magnitude of Watts while a typical user equipment detects signals of the order of magnitude of uWatts. The rest of the energy is either
dissipated over the channel or is a source of interference for other network elements.
In this talk, we challenge this status quo and introduce “Wireless 2.0”: The future generation of wireless communication networks, in which the radio environment becomes controllable and intelligent by leveraging the emerging technologies of reconfigurable intelligent surfaces (RIS) and artificial intelligence (AI). Wireless 2.0 is a paradigm-shifting wireless vision according to which wireless networks are equipped with the functionalities of (i) customizing the radio environment (i.e., controlling the propagation of radio waves and environmental objects) besides the capability of optimizing the end-points of the communication links; and (ii) optimizing the resulting wireless communication and networks with the aid of AI-based computational methods.
Speakers, Panelists and Moderators
MARCO DI RENZOProfessor, FellowIEEE Standards AssociationMarco Di Renzo was born in L’Aquila, Italy, in 1978. He 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 a 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 CentraleSupelec, Paris, France. He is also a Nokia Foundation Visiting Professor at Aalto University, Helsinki, Finland, and was an Honorary Professor at University Technology Sydney, Sydney, Australia, and a Visiting Professor at University of L’Aquila, Italy. In Paris-Saclay University, he is a Member of the coordinating committee of the Ph.D. school on Information and Communication Technologies, and the Coordinator of the “Intelligent Networks” research cluster within the DigiCosme Laboratory of Excellence. He serves as the Editor-in-Chief of IEEE Communications Letters. He served as an Editor of IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Communications Letters, and as the Associate Editor-in-Chief of IEEE Communications Letters. He is a Distinguished Lecturer of the IEEE Vehicular Technology Society and IEEE Communications Society, and a Member of the Emerging Technology Committee of the IEEE Communications Society. He is a recipient of several awards, including the 2013 IEEE-COMSOC Best Young Researcher Award for Europe, Middle East and Africa, the 2013 NoE-NEWCOM# Best Paper Award, the 2014-2015 Royal Academy of Engineering Distinguished Visiting Fellowship, the 2015 IEEE Jack Neubauer Memorial Best System Paper Award, the 2015 CNRS Award for Excellence in Research and Ph.D. Supervision, the 2016 MSCA Global Fellowship (declined), the 2017 SEE-IEEE Alain Glavieux Award, the 2018 IEEE-COMSOC Young Professional in Academia Award, the 2019 Nokia Foundation Visiting Professorship, and 8 Best Paper Awards at IEEE conferences (2012 and 2014 IEEE CAMAD, 2013 IEEE VTC-Fall, 2014 IEEE ATC, 2015 IEEE ComManTel, 2017 IEEE SigTelCom, EAI 2018 INISCOM, IEEE ICC 2019). He is a Highly Cited Researcher according to Clarivate Analytics and Web of Science, and a Fellow of the IEEE. Marco Di Renzo was the Lead Guest Editor of the Special Issue on Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces of the IEEE Journal on Selected Areas in Communications, and is currently a Guest Editor of three Special Issues on Reconfigurable Intelligent Surfaces in IEEE Transactions on Cognitive Communications and Networking, China Communications, and IEEE Wireless Communications Magazine. Also, he is the Proponent and Lead Contributor of the Best Readings in Reconfigurable Intelligent Surfaces of the IEEE Communications Society (to appear). Marco Di Renzo is the Founding Chair of the Special Interest Group on Reconfigurable Intelligent Surfaces for Smart Radio Environments (RISE), Wireless Communications Technical Committee of the IEEE Communications Society, the Emerging Technology Committee Liaison Officer of the Special Interest Group on Reconfigurable Intelligent Surfaces for Signal Processing and Communications (REFLECTIONS), Signal Processing and Computing for Communications Technical Committee of the IEEE Communications Society, and the Emerging Technology Committee Liaison Officer of the Emerging Technology Initiative on Reconfigurable Intelligent Surfaces for Smart Radio Environments of the IEEE Communications Society. Marco Di Renzo is a Founding Officer of the Emerging Technology Committee of the IEEE Communicatins Society on Machine Learning for Communications, and a Principal Investigator of the H2020 European-funded project ARIADNE (Artificial Intelligence Aided D-band Network for 5G Long Term Evolution) on metasurface-aided AI communications systems beyond 5G.