Lecturers
Prof. Alessio Zappone (University of Cassino and Southern Lazio, Italy)
Alessio Zappone obtained his Ph.D. degree in electrical engineering in 2011 from the University of Cassino and Southern Lazio, Cassino, Italy. His Ph.D. studies were focused on distributed algorithms for energy-efficient resource allocation in wireless networks. After obtaining his Ph.D. Alessio has been with the Technische Universit\”at Dresden, Germany, managing the project CEMRIN on energy-efficient resource allocation in wireless networks, funded by the German Research Foundation. From 2017 to 2019 he has been the recipient of the H2020 Individual Marie Curie fellowship for experienced researchers BESMART, carried out in the LANEAS group of CentraleSupelec, Paris, France. He is now a tenured professor at the university of Cassino and Southern Lazio, Italy. He received the Marconi Prize paper award of the IEEE communication society with a paper on resource allocation for RIS-based networks. He was appointed exemplary reviewer for the IEEE Transactions on Communications and IEEE Transactions on Wireless Communications in 2017. Alessio is an IEEE Senior Member, serves as senior area editor for the IEEE Signal Processing Letters, Editor of the IEEE Transactions on Wireless Communications, and has served as guest editor for the IEEE Journal on Selected Areas on Communications (Special Issues on Energy-Efficient Techniques for 5G Wireless Communication Systems and on Wireless Networks Empowered by RIS). Alessio is a co-founder and chair of the special interest group REFLECTIONS, activated within the Signal Processing and Computing for Communications Technical Committee of the IEEE Communications Society, which focuses on the use of RIS for signal processing and communications. He is also a co-founder and vice-chair of the IEEE emerging technology initiative (ETI) on RIS, activated by the IEEE communication society.
Prof. Marco Di Renzo (Université Paris Saclay , France)
Marco Di Renzo (Fellow, IEEE) received the Ph.D. degrees in electrical engineering from the University of L’Aquila, Italy, in 2007. 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 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. Also, he serves as the Founding Chair of the Special Interest Group RISE 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. In addition, he is a Co-Founder and the Emerging Technology Committee Liaison Officer of the Special Interest Group REFLECTIONS on Reconfigurable Intelligent Surfaces of the Signal Processing and Computing for Communications Technical Committee of the IEEE Communications Society and a Co-Founder and the Emerging Technology Committee Liaison Officer of the Emerging Technology Initiative on Reconfigurable Intelligent Surfaces. He is a Highly Cited Researcher (Clarivate Analytics, Web of Science), a World’s Top 2% Scientist from Stanford University, a Fellow of IEEE and IET. He has received 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, and in 2021 the Fulbright Fellowship to work on metamaterial-based wireless CUNY Advanced Science Research Center, USA. He received the 2021 EURASIP Best Paper Award for a paper on Reconfigurable Intelligent Surfaces and Smart Radio Environments. Finally, Marco Di Renzo has served as a Guest Editor of several special issues on Reconfigurable Intelligent Surfaces, which include the first Special Issue on the topic published in November 2020 in the IEEE Journal on Selected Areas in Communications, as well as a Guest Editor IEEE Journal on Selected Areas in Signal Processing, IEEE Access, IEEE Wireless Communications Magazine, IEEE Transactions on Cognitive Communications and Networking, IET Communications, China Communications, and a Workshop Organizer on Reconfigurable Intelligent Surfaces at 2020 IEEE GLOBECOM, 2021 IEEE WCNC, and 2021 IEEE ICC. Finally, Marco Di Renzo is the Vice-Chair of the Industry Specification Group on Reconfigurable Intelligent Surfaces within the European Telecommunications Standards Institute.
Abstract
The tutorial starts by discussing 5G standardization activities, the performance that 5G networks will be able to grant, and how this appears inadequate to keep the pace with the exponentially increasing number of connected devices and with the rise of many new heterogeneous services. The main challenges that stand in our way towards meeting the requirements of 6G will be identified, namely the extreme heterogeneity of the tasks to execute, which range from broadband communications, to very low-latency communications, extreme energy efficiency and high data rates, and localization. The use of RIS to enable this 6G vision will be discussed. Both an academic and industrial perspective will be provided. Moreover, the economical and societal opportunities that overcoming 5G holds will be analyzed. After this first part, the audience will have a proper understanding of the main principles that make the RIS technology possible, of the potential of RISs, and of the challenges and opportunities related to overcoming 5G networks.
Motivation and Context
Between 2020 and 2030, the number of IP connections will rise by 55% annually, reaching 5,016 exabytes in 2030. Moreover, future wireless networks will have to support many innovative services, each with its own specific requirements, e.g. end-to-end latency of 1ns, reliability higher than 99.999%, terminal densities of 1 million per square kilometer, per-user data-rate of the order of Tera-bit/s, terminal location accuracy of the order of 0.1m. These requirements are beyond what 5G networks have been designed to handle.
A recent technological breakthrough which can revolutionize the traditional approach to network design and operation is that of reconfigurable intelligent surfaces (RISs). RIS-based communications put forth the idea of treating the communication environment not as an entity fixed by nature, but as a variable to be customized. RISs are nearly-passive structures with very limited power consumption, size, and deployment costs. RISs are planar structures made of special materials, known as meta-materials, on which electromagnetic reflectors are placed and spaced at sub-wavelength distances. RISs can reflect/refract the incoming electromagnetic signal in directions that are not bound by Snell’s reflection and diffraction laws, but that instead can be fully customized. Moreover, a RIS provides the possibility of adapting its electromagnetic response in real-time in response to the sudden changes in the network and/or in the traffic demands. RISs can be deployed on the walls of buildings or can be used to coat the environmental objects between the communicating devices, which effectively makes the wireless channel a new variable to be optimized. Moreover, thanks to their reduced size and cost, an RIS can be equipped with a number of electromagnetic reflectors that is significantly larger than the number of antennas of an active (massive) MIMO array.
Structure and Content
Introduction. The tutorial starts by discussing 5G standardization activities, the performance that 5G networks will be able to grant, and how this appears inadequate to keep the pace with the exponentially increasing number of connected devices and with the rise of many new heterogeneous services. The main challenges that stand in our way towards meeting the requirements of 6G will be identified, namely the extreme heterogeneity of the tasks to execute, which range from broadband communications, to very low-latency communications, extreme energy efficiency and high data rates, and localization. The use of RIS to enable this 6G vision will be discussed. Both an academic and industrial perspective will be provided. Moreover, the economical and societal opportunities that overcoming 5G holds will be analyzed. After this first part, the audience will have a proper understanding of the main principles that make the RIS technology possible, of the potential of RISs, and of the challenges and opportunities related to overcoming 5G networks.
Modeling RIS-based wireless network. This part of the tutorial will discuss how to model communication systems based on RISs, highlighting all of their peculiarities and advantages compared to traditional systems and presenting concrete use-cases, application scenarios, and business models. Specifically, the tutorial will introduce novel electromagnetic-based models of RIS, showing how they can be employed to model RIS-based wireless networks, in order to come to new expressions of the received power in a RIS-based communication channel. It will be shown how the unique properties of RISs are expected to yield different scaling laws from those currently encountered in wireless networks, e.g., a different received power expression as a function of the distance between transmitters and receivers, or a received SNR as a function of the number of reflecting elements equipped at the RIS. As a result of this discussion, the advantages and limitations of RISs will be discussed, in comparison with other more traditional transmission technologies, such as massive MIMO and relaying. The tutorial will elaborate on how RISs can be used for improving the performance of wireless networks, e.g., for communication at high frequency bands, such as the mmWave frequency range. The electromagnetic models introduced in this part of the tutorial will be supported by experimental results obtained through the prototype testbed for RIS-based communications described in reference.
Design of RIS-based wireless networks. This part of the tutorial will address the most recent results and techniques for the optimization of RIS-based wireless networks. At first, the new optimization challenges posed by the use of RISs will be identified, and a thorough literature survey about resource allocation for RIS-based wireless networks design will be given. Namely, the fact that the RIS is a (nearly) passive device without neither dedicated transmit and receive hardware, nor a digital signal processor, results in: 1) more challenging resource allocation problems to solve; 2) more sophisticated channel estimation and feedback protocols, which poses additional constraints on the resource allocation algorithms, and leads to the need of performing overhead-aware resource allocation as well as joint channel estimation and resource allocation, unlike what typically happens for the design of present wireless communication systems. The latest techniques for resource allocation in RIS-based wireless networks will be discussed.
Both point-to-point and multi-user MIMO wireless networks will be considered and it will be shown how to handle the more challenging resource allocation problems encountered when designing RIS-based wireless networks. Different performance metrics will be optimized, including the system spectral efficiency, energy efficiency, and their trade-off, with respect to the RIS phase shifts, the number of RIS reflecting elements, the transmit powers, transmit beamforming, receive filters, and communication bandwidth.
The resource allocation algorithms will be tailored to the electromagnetic RIS models developed in the previous phase of the tutorial, and will address also the realistic cases in which the RIS phase shifts can only take discrete values as well as the case in which the moduli and phases of the RIS reflection coefficients can not be set independently but instead a functional relationship exists between the two. Moreover, the algorithms will fully account for the overhead related to channel estimation and for setting up the optimal configuration of the RIS elements. This will be carried out with reference to novel procedures for channel estimation in RIS-based networks based on least-squares and MMSE estimation approaches. The tutorial will also describe how to optimize RIS-based networks with electromagnetic field exposure constraints. Specifically, the design of RIS-based networks will be carried out in the scenario in which not only power and unit-norm beamformer constraints are enforced, but also guaranteeing to limit the intensity of the electromagnetic radiation towards specific locations. It will be shown how an RIS can be beneficial in maintaining satisfactory rate performance, while at the same time reducing the electromagnetic field exposure caused to other network nodes.
The performance of the resource allocation routines described in this part of the tutorial will be tested by experimental results obtained through the prototype testbed for RIS-based communications described in reference.
Conclusions. The tutorial will end by summarizing the take-home points of the tutorial, and highlighting the most relevant research directions and open problems to be investigated towards the development of beyond 5G RIS-based wireless networks.
Assuming an average duration of 3 hours, the detailed outline of the tutorial is as follows:
Introduction, fundamentals, and experimental results (30 min. – M. Di Renzo)
- Overcoming 5G by RISs – A 6G vision.
- Meta-materials technology for RISs.
Modeling RIS-based networks (45 min. – M. Di Renzo)
- Electromagnetic models for RIS-based networks.
- Scaling laws for large RIS-based networks.
- Comparison with other transmission technologies.
- Experimental validation results.
Design of RIS-based networks (90 min. – A. Zappone)
- Spectral and energy efficiency maximization of RIS-based networks with realistic electromagnetic models.
- Joint channel estimation and resource allocation in RIS-based networks with realistic electromagnetic models.
- Design of RIS-based networks with electromagnetic field exposure constraints.
- Experimental validation results.
Conclusions (15 min. – M. Di Renzo)
- Take-home messages
- Open challenges and future research directions