SPAWC21 is hosting 12 live-streamed SPAWC talks (STs) of 30 minutes each from senior researcher from academia and industry, focusing on timely topics in signal processing for wireless communications. The list of distinguished speakers is reported below, in alphabetical order.
Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets.
On the other hand, signal processing and communications have traditionally relied on classical statistical modeling techniques that utilize mathematical formulations representing the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. Here we introduce various approaches to model based learning which merge parametric models with optimization tools leading to efficient, interpretable networks from reasonably sized training sets. We will consider examples of such model-based deep networks to a variety of problems in signal processing and communications.
Yonina Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel, where the heads the center for biomedical engineering. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering both from Tel-Aviv University (TAU), Tel-Aviv, Israel, in 1995 and 1996, respectively, and the Ph.D. degree in electrical engineering and computer science from MIT in 2002. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), the Award for Women with Distinguished Contributions, the Andre and Bella Meyer Lectureship, the Career Development Chair at the Technion, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion’s Award for Excellence in Teaching (two times). She received several best paper awards and best demo awards together with her research students and colleagues, was selected as one of the 50 most influential women in Israel, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing.”
Wireless networks are incorporating mmWave spectrum and beyond. This, together with MIMO technology using large antenna arrays, provides the key ingredients to develop integrated communication and sensing systems that exploit the similarities between the required hardware, the signal processing algorithms or the sensing and communication channels. In this talk, I provide an overview of signal processing techniques that enable different types of integration between sensing and communication. First, I explain the joint design of communication and radar functionalities using the same hardware. Second, I describe alternative strategies that pursue a fruitful collaboration among collocated sensing and communication modules which may be using different hardware or radio resources. Then, I review the opposite setting: communications-aided sensing and applications where communication instead acts as side information for enhanced sensing. Finally, I describe the ongoing work and challenges associated with configuring smart environments for joint/collaborative MIMO communication and sensing using intelligent surfaces. Throughout the talk, I make the case that time is right for communication and sensing to be considered together. I also explain why communication and sensing will likely to be THE defining physical layer feature of 6G. This means that signal processing engineers must play an important role in the development of next generation wireless systems.
Nuria González Prelcic received her Ph.D. in Electrical Engineering in 2000 from the University of Vigo, Spain. She joined the faculty at NC State as an Associate Professor in 2020. She was previously an Associate Professor in the Signal Theory and Communications Department at the University of Vigo, Spain, and also hold visiting positions at the University of Texas at Austin and the University of New Mexico. She was also the founding director of the Atlantic Research Center for Information and Communication Technologies (atlanTTic) at the University of Vigo (2008-2017). She is an Editor for IEEE Transactions on Wireless Communications. She is an elected member of the IEEE Sensor Array and Multichannel Technical Committee. She is a member of the IEEE SPS Integrated Sensing and Communication Technical Working Group. She has published more than 80 papers in the topic of signal processing for millimeter-wave communications, including a highly cited tutorial published in the IEEE Journal of Selected Topics in Signal Processing which has received the 2020 IEEE SPS Donald G. Fink Overview Paper Award.
We thoroughly analyze the rate region provided by the asynchronous transmission in multiple access channels (MACs). We derive the corresponding theoretical capacity-regions, applicable to a wide range of pulse shaping methods. We analytically prove that asynchronous transmission enlarges the capacity-region of MACs. Although successive interference cancellation (SIC) can achieve the optimal sum-rate for the conventional uplink non-orthogonal multiple access (NOMA) methods, it is unable to achieve the boundary of the capacity-region for the asynchronous transmission. We demonstrate that for the asynchronous transmission, the optimal SIC decoding order to achieve the maximum sum-rate is based on the users’ channel strengths. This optimal ordering is in contrast to the conventional uplink NOMA, where various decoding orders can result in the maximum sum-rate.
Hamid Jafarkhani is a Chancellor’s Professor at the Department of Electrical Engineering and Computer Science, University of California, Irvine, where he is also the Director of Center for Pervasive Communications and Computing, the Co-Director of Networked Systems Program, and the Conexant-Broadcom Endowed Chair. Among his awards are the NSF Career Award, the UCI Distinguished Mid-Career Faculty Award for Research, the School of Engineering Excellence in Research Senior Career Award, the IEEE Marconi Prize Paper Award in Wireless Communications, the IEEE Communications Society Award for Advances in Communication, the IEEE Wireless Communications Technical Committee Recognition Award, and the IEEE Eric E. Sumner Award. Dr. Jafarkhani is listed as an ISI highly cited researcher. According to the Thomson Scientific, he was one of the top 10 most-cited researchers in the field of “computer science” for 1997-2007. He is the 2017 Innovation Hall of Fame Inductee at the University of Maryland’s School of Engineering. He is a Fellow of AAAS, an IEEE Fellow, and the author of the book “Space-Time Coding: Theory and Practice.”
In this talk, I will show how, starting from the field of waves in complex media in acoustics and RF, we learned how to control the propagation of light through very scattering media using smart reconfigurable reflectors, namely spatial light modulators. I will explain how this has led us, 8 years ago, to propose to use tunable metasurfaces as smart reflectors to enhance wireless communications. I will show the first results obtained and published in 2014, that proved how a small tunable metasurface placed in an office room can multiply by 10 the energy transmitted between 2 antennas. I will propose a basic application to wireless communications in the context of WIFI, obtained by the company years ago, thus showing the first use of a RIS in a communication system. Then, I will underline what we believe is important to have in mind in terms of wave control versus RIS complexity, notably when it comes to practical applications. I will show a few examples of RIS developed at Greenerwave, from 1 GHz to 77 GHz, and briefly describe their applications. To finish, I will show first examples of RIS aided wireless communications realized in the mmWave range, demonstrating non-LOS mmWave data transmission using low complexity and low consumption tunable metasurfaces. Finally, I will say a few words about my vision of RIS in the future, for both low and high frequencies.
Geoffroy Lerosey is the co-founder and Chief Scientific Officer of Greenerwave. Geoffroy earned an engineering degree from ESPCI Paris, a PhD in Physics from Université Paris Diderot and spent a postdoctoral year at University of California at Berkeley. Before founding Greenerwave, Geoffroy worked 10 years for French CNRS, at Institut Langevin, on novel wave control approaches as well as metamaterials and metasurfaces. Geoffroy was invited to more than 80 international conferences. His research led to 100 scientific articles, 15 patents and 2 startups.
With the advent of the 5G NR standard, data rates of a few Gbps have to be supported on the user link. For future evolutions of wireless communication standards, even higher data rates should be expected, posing the challenge of designing low-complexity channel code decoders. In this talk, we will briefly review some recently proposed codes and decoding algorithms for very high throughput applications. We will then discuss a class of decoding algorithms that have been shown to support decoder implementations for data rates beyond 100 Gbps, in the domain of optical communication systems, and we will outline some open challenges that need to be addressed in the case of wireless networks.
Gianluigi Liva received the M.S. and Ph.D. degrees in electrical engineering from the University of Bologna, Italy, in 2002 and 2006, respectively. From 2004 to 2005, he was visiting the University of Arizona in Tucson. Since 2006, he has been with the Institute of Communications and Navigation, German Aerospace Center (DLR), where he currently leads the Information Transmission Group. In 2010, he has been appointed as a Lecturer of channel coding with the Institute for Communications Engineering (LNT), Technical University of Munich (TUM). Since 2014, he has been a Lecturer of channel codes with iterative decoding with LNT, TUM. His main research interests include satellite communications, random access techniques, and error control coding for wireless and optical links. He is/has been active in the DVB-SH, DVB-RCS, and DVB-S2 standardization groups, and in the standardization of error correcting codes for deep-space communications within the CCSDS. Since 2020, he serves as Associate Editor in Coding and Information Theory for the IEEE Transactions on Communications.
Massive MIMO is the most spectrally efficient wireless scheme yet devised. At some point -probably not in 5G- Massive MIMO will be fully developed and exploited. Is this the end of the line for wireless innovation, or can we discover something better? To address this existential question, we need to consider what fundamental physics has to say about wireless communications. The answer is encouraging: as far as is known, no existing or projected wireless system operates anywhere near fundamental limits imposed by nature. Lack of working knowledge of electromagnetic theory is a serious obstacle to the contribution of many communications and signal processing researchers to physics-based communication theory. To remedy this, I have developed a new graduate course at NYU, “A Linear System Approach to Wave Propagation”, which replaces the traditional physicist’s treatment of Maxwell’s equations, based on potentials and the method of separation of variables, with a frequency/wavenumber Fourier transform solution better suited for engineering problems.
Thomas Marzetta is Distinguished Industry Professor at NYU Tandon School of Engineering, ECE Department, and Director of NYU WIRELESS. He received the PhD and SB in Electrical Engineering from MIT in 1978 and 1972, and the MS in Systems Engineering from University of Pennsylvania in 1973. Prior to joining NYU in 2017, he had three industrial research careers: petroleum exploration (Schlumberger-Doll Research, 1978–1987), defense (Nichols Research Corporation, 1987–1995), and telecommunications (Bell Labs, 1995–2017). At Bell Labs, he directed the Communications and Statistical Sciences Department within the former Mathematical Sciences Research Center, and he was elected a Bell Labs Fellow. He originated Massive MIMO, one of the cornerstones of 5G wireless technology. He is lead-author of the book “Fundamentals of Massive MIMO”. Prof. Marzetta was elected a member of National Academy of Engineering in 2020. Additional recognition for his contributions to Massive MIMO include the 2019 Radio Club of America Armstrong Medal, the 2017 IEEE Communications Society Industrial Innovation Award, the 2015 IEEE Stephen O. Rice Prize, and the 2015 IEEE W. R. G. Baker Award. He was elected a Fellow of the IEEE in 2003, and he received an Honorary Doctorate from Linköping University, Sweden, in 2015.
Millimeter wave (mmWave) and Terahertz (THz) frequency bands are the next frontier in wireless communication. The available bandwidth is orders of magnitude higher than in existing systems, and the small carrier wavelengths enable realization of antenna arrays with a large number of elements within reasonable form factor. While this offers unprecedented scaling of available spatiotemporal degrees of freedom, realizing this potential requires moving beyond the RF and hybrid beamforming architectures and moderate bandwidths used in existing mmWave systems. Recent advances in mmWave RFIC design indicate that all-digital multiuser MIMO and LoS MIMO systems supporting bandwidths of the order of 10 GHz are on the cusp of feasibility. However, the realization of such all-digital systems requires overcoming or sidestepping significant hardware bottlenecks, including RF nonlinearities, phase noise, and the low precision of the analog-to-digital converters (ADCs) available at multi-GHz sampling rates. In this talk, we discuss co-design of hardware and signal processing to sidestep and alleviate such bottlenecks, taking advantage of the increased number of antenna elements.
Upamanyu Madhow is Distinguished Professor of Electrical and Computer Engineering at the University of California, Santa Barbara. His current research interests focus on next generation communication, sensing and inference infrastructures centered around millimeter wave systems, and on robust machine learning. Dr. Madhow is an IEEE Fellow, recipient of the 1996 NSF CAREER award, co-recipient of the 2012 IEEE Marconi prize paper award in wireless communications, and recipient of a Distinguished Alumni award in 2018 from the ECE department at the University of Illinois at Urbana-Champaign. He is the author of two textbooks published by Cambridge University Press, Fundamentals of Digital Communication (2008) and Introduction to Communication Systems (2014). He is co-inventor on 30 US patents, and has been involved in several successful technology transfer efforts.
After a decade of intensive research in massive MIMO, the scientific community has developed fundamental theory to characterize the operation of this technology. To date, the development of massive MIMO has been exclusively based on information theory (IT) tailored towards cellular communications. While IT is undoubtedly a versatile mathematical tool, it is based on mathematical logic. This theoretical framework now needs to be extended and reshaped to: (i) account for the unique electromagnetic properties and (ii) incorporate the main feature of future massive MIMO -based communication systems, namely their capability of sensing the system’s response to the radio waves, and thereby informing its modification. In this presentation, we will overview some recent advances in this space and discuss the open challenges for the upcoming years.
Michalis Matthaiou is a Chair Professor of Communications Engineering and Signal Processing and Deputy Director of the Centre for Wireless Innovation (CWI) at Queen’s University Belfast, U.K. He has published in excess of 220 papers, including some 100 IEEE journal papers. Dr. Matthaiou and his coauthors received the IEEE Leonard G. Abraham Prize in 2017. He currently holds the ERC Consolidator Grant BEATRICE (2021-2026), focused on the interface between information and electromagnetic theories. He was awarded the prestigious 2018/2019 Royal Academy of Engineering/The Leverhulme Trust Senior Research Fellowship and also received the 2019 EURASIP Early Career Award. His team was also the Grand Winner of the 2019 Mobile World Congress Challenge. He is currently the Editor-in-Chief of Elsevier Physical Communication, a Senior Editor for IEEE Wireless Communications Letters and an Associate Editor for the IEEE JSAC Series on Machine Learning for Communications and Networks.
In this talk, we will consider the problem of learning high-dimensional distributions and their parameters under communication and local differential privacy constraints. We will develop novel encoding and decoding mechanisms that simultaneously achieve optimal privacy and communication efficiency in various canonical settings, including both probabilistic and so called “distribution-free” models (e.g. distribution estimation, frequency estimation, distributed mean estimation etc.). We will complement our achievability results with information-theoretic lower bounds based on data processing inequalities that describe how Fisher information from statistical samples scales with local differential privacy and communication constraints. We will also compare the performance of digital and analog communication schemes. We will conclude by discussing applications of our results to federated and distributed learning.
Ayfer Ozgur received her B.Sc. degrees in electrical engineering and physics from Middle East Technical University, Turkey, in 2001 and the M.Sc. degree in communications from the same university in 2004. From 2001 to 2004, she worked as hardware engineer for the Defense Industries Development Institute in Turkey. She received her Ph.D. degree in 2009 from the Information Processing Group at EPFL, Switzerland. In 2010 and 2011, she was a post-doctoral scholar at the same institution. She is currently an Associate Professor in the Electrical Engineering Department at Stanford University where she is the Chambers Faculty Scholar in the School of Engineering. Dr. Ozgur received the EPFL Best Ph.D. Thesis Award in 2010, the NSF CAREER award in 2013, the Okawa Foundation Research Grant and the IEEE Communication Theory Technical Committee (CTTC) Early Achievement Award in 2018, and was selected as the inaugural Goldsmith Lecturer of the IEEE ITSoc in 2020.
Recently, deep learning (DL) based autoencoders (AE) have appeared as a potential near-optimal solution for designing wireless communications systems. Near-optimal coded modulation and joint source-channel coding designs of high-rate short block codes with bit-labelling have been achieved. In this talk, firstly, to tackle fading channels, we show that DL-based AE frameworks can perform near-optimal coded modulation and differential coded modulation in the presence and absence of channel state information. Secondly, we focus on interpretability and understand the AE-based frameworks from an information-theoretic perspective. Thirdly, we focus on robustness of the AE frameworks defined by three principles – (i) reusability to different scenarios, (ii) reproducibility, and (iii) transferability to other rates. Lastly, we design and develop AE-based frameworks in scenarios such as relaying networks, multiuser downlink, full-duplex networks, and non-orthogonal multiple access.
Mathini Sellathurai (Senior Member, IEEE) is currently a Professor of signal processing and wireless communications and Dean of Science and Engineering with Heriot-Watt University, Edinburgh, U.K. She has been active in signal processing research for the past 20 years and has a strong international track record in multiple-input, multiple-output (MIMO) signal processing with applications in radar, and wireless communications. She held visiting positions with Bell-Laboratories, Holmdel, USA, and at The Canadian Communications Research Centre, Ottawa, Canada. She has published over 200 peer reviewed papers in leading international journals and IEEE conferences, given invited talks and has written several book chapters as well as a research monograph. She is a recipient of an IEEE Fred W. Ellersick Best Paper Award in 2005, the Industry Canada Public Service Awards for contributions to Science and Technology in 2005, and Awards for contributions to Technology Transfers to Industry in 2004. She was the recipient of the Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Award for her Ph.D. dissertation in 2002. She was an Editor for IEEE TRANSACTIONS ON SIGNAL PROCESSING from 2009 to 2014, and from 2015 to 2018, the General Co-Chair of IEEE SPAWC2016 in Edinburgh, and a member for IEEE SPCOM Technical Strategy Committee from 2014 to 2019.
The future of connectivity is in the creation of digital twin worlds that are a true representation of the physical and biological worlds at every spatial and time instant, unifying our experience across these physical, biological and digital worlds. Augmented proximity, augmented intelligence, and augmented productivity powered by precision sensing and actuation, ubiquitous computing, knowledge systems, and multi-sensory rendering will drive new requirements and technologies for 6G connectivity. Novel signal processing methods will be needed for native machine learning based air-interface and joint communication and sensing, which are expected to be the disruptive technologies for 6G. We will discuss the evolution of the application of deep learning to mobile cellular systems with some examples highlighting the potential benefits. We will then present signal processing for precision indoor localization and identify several key challenges to be addressed in the future.
Dr. Harish Viswanathan is Head of Radio Systems Research Group in Nokia Bell Labs. He received the B. Tech. degree from the Department of Electrical Engineering, Indian Institute of Technology, Chennai, India and the M.S. and Ph.D. degrees from the School of Electrical Engineering, Cornell University, Ithaca, NY. Since joining Bell Labs, he has worked extensively on wireless research ranging from physical layer to network architecture and protocols including multiple antenna technology for cellular wireless networks, multi-hop relays, network optimization, network architecture, and IoT communications. He has published extensively with over 100 publications. From 2007 to 2015, Harish was in the Corp CTO organization, where as a CTO Partner he advised the Corporate CTO on Technology Strategy through in-depth analysis of emerging technology and market needs. He is a Fellow of the IEEE and a Bell Labs Fellow.
Conventional random access protocols are contention based. This talk will examine the cost and benefit of scheduling-based strategies in massive random access, where among a large number of N potential devices, a small fraction of K devices request transmission at any given time. We show that a scheduled random access scheme can significantly improve the performance of uncoordinated coded pilot access in a massive MIMO system. Further, the information theoretical limit on the amount of feedback needed to ensure collision-free scheduling of K active devices out of N potential devices is in fact independent of N. The minimum amount of feedback can be made as low as (K+1)log(e) bits, instead of K log(N) bits required by conventional feedback schemes.
Wei Yu is Professor and Canada Research Chair in Information Theory and Wireless Communications at the University of Toronto. He received a Ph.D. degree from Stanford University in 2002. He is a Fellow of IEEE, a Fellow of the Canadian Academy of Engineering, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. He received the IEEE Marconi Prize Paper Award in Wireless Communications in 2019, the IEEE Communications Society Award for Advances in Communication in 2019, the IEEE Signal Processing Society Best Paper Award in 2017 and 2008, and the IEEE Communications Society Best Tutorial Paper Award in 2015. He is the President of the IEEE Information Theory Society in 2021.