Abstract: Wireless systems have traditionally been designed to operate under the channel conditions provided by nature. The advent of reconfigurable intelligent surfaces (RIS) changes the status quo by adding the ability to control wave propagation. A RIS is an array of reflecting elements with properties that can be tuned in real-time to synthesize the reflection behavior of a differently shaped object. This feature is helpful for both communications and localization. There are many ways that a RIS can improve the wireless channel between a transmitter and a receiver. The larger the surface is, the higher the potential communication performance improvement. The gains are achieved by adapting the RIS configuration to the channel coefficients from the transmitter to the receiver via the RIS, which must be estimated in practice. The estimation challenge grows with the number of reflecting elements, in terms of computational complexity and signal resources required for pilot signaling. The problem differs greatly from the classical estimation problems in multi-antenna communications because the RIS is blind and substantially larger. Hence, there is a substantial risk that the estimation dimensionality will be the showstopper in practice. In this talk, we will look at how array signal processing theory provides suitable tools to describe and analyze RIS systems. We will first explore how these tools enable us to model the physical channels that interact with a RIS. We will then simplify the channel estimation problem by exploiting the fundamental channel properties, such as the array geometry, array responses, and channel geometry. It turns out that, under the right circumstances, array processing methodology is the key to making RIS a practically viable technology.

Bio: Emil Björnson is a Professor of Wireless Communication at the KTH Royal Institute of Technology, Stockholm, Sweden. He is an IEEE Fellow, Digital Futures Fellow, and Wallenberg Academy Fellow. He has a podcast and YouTube channel called Wireless Future. His research focuses on multi-antenna communications and radio resource management, using methods from communication theory, signal processing, and machine learning. He has authored three textbooks and has published a large amount of simulation code. He has received the 2018 and 2022 IEEE Marconi Prize Paper Awards in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society Fred W. Ellersick Prize, the 2019 IEEE Signal Processing Magazine Best Column Award, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award, the 2020 CTTC Early Achievement Award, the 2021 IEEE ComSoc RCC Early Achievement Award, and the 2023 IEEE Communications Society Outstanding Paper Award. His work has also received six Best Paper Awards at conferences.

Abstract: In many fields today, such as neuroscience, remote sensing, computational social science, and physical sciences, multiple sets of data are readily available. Matrix and tensor factorizations enable joint analysis, i.e., fusion of these multiple datasets such that they can fully interact and inform each other while also minimizing the assumptions placed on their inherent relationships. A key advantage of these methods is the direct interpretability of their results. This talk presents an overview of models based on independent component analysis (ICA), and its generalization to multiple datasets, independent vector analysis (IVA) with examples in fusion and analysis of neuroimaging data. Relationship of IVA to other methods such as multiset canonical correlation analysis (MCCA) and PARAFAC2 is discussed, highlighting a number of new research directions. Importance of computational reproducibility is also addressed, with a focus on its relationship to model match and interpretability.

Bio: Tülay ADALI is a Distinguished University Professor at the University of Maryland Baltimore County (UMBC), Baltimore, MD. She received the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 1992 and joined the faculty at UMBC the same year. Over the years, she has served the IEEE and the IEEE Signal Processing Society (SPS) in numerous capacities. She is currently the Chair of the IEEE Brain Technical Community and served as the Signal Processing Society (SPS) Vice President for Technical Directions 2019−2022. She has been part of the organizing committees of many conferences and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Technical Chair (2017), Special Sessions Chair (2018 and 2024), Publicity Chair (2000 and 2005), and Publications (2008). She was the General/Technical Chair for the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing (NNSP) Workshops 2001−2009, and 2014, and she is General Chair for 2023 MLSP. She served multiple terms in three technical committees of the SPS (NNSP/MLSP, Bio Imaging and Signal Processing, Signal Processing Theory and Methods) and chaired the NNSP/MLSP Technical Committee, 2003–2005 and 2011–2013. She served or is currently serving on the editorial board of multiple journals, including the IEEE Transactions on Signal Processing, Proceedings of the IEEE, and the IEEE Journal of Selected Topics in Signal Processing. Prof. Adali is a Fellow of the IEEE, AIMBE, and AAIA, a Fulbright Scholar, and an IEEE SPS Distinguished Lecturer. She is the recipient of the SPS Meritorious Service Award, Humboldt Research Award, IEEE SPS Best Paper Award, the SPIE Unsupervised Learning and ICA Pioneer Award, the University System of Maryland Regents'' Award for Research, and the NSF CAREER Award.

Abstract: Future generations of cellular and IoT networks will operate in the upper millimeter wave (mmW) frequency band where ≥ 10 GHz bandwidth can be used to meet the ever-increasing demands. While these abundant spectrum resources have been leveraged to increase data rates in 5G cellular, the next goal is to further diversify 5G performance for applications requiring low latency, high reliability, and massive connectivity. These new requirements demand fundamental rethinking of radio architectures, signal processing, and networking protocols to address challenges of extremely wide bandwidths, large antenna array sizes, and high cell density at mmW frequencies. In this talk, we will discuss latency and connectivity bottlenecks from a physical layer and network layer perspective in the context of mmW propagation where the beamformed initial access, beam training and tracking are critical for reliability and coverage. While increasing the antenna array size and bandwidth in principle provide benefits of improved SNR and capacity, at the same time the hardware complexity and radio impairments require careful design of the signal processing algorithms. We will introduce a novel analog True-Time-Delay (TTD) antenna array as a truly ultra-wideband scalable antenna array architecture and demonstrate its unique frequency-dependent beamforming modalities for fast beam training and channel estimation, massive IoT connectivity and flexible beam to sub-band assignment for cellular OFDMA transmission.

Bio: Danijela Cabric is a Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles. She received M.S. from the University of California, Los Angeles in 2001 and Ph.D. from University of California, Berkeley in 2007, both in Electrical Engineering. In 2008, she joined UCLA as an Assistant Professor, where she heads Cognitive Reconfigurable Embedded Systems lab. Her current research projects include novel radio architectures, signal processing, communications, machine learning and networking techniques for spectrum sharing, 5G millimeter-wave, massive MIMO and IoT systems. She is a principal investigator in the three large cross-disciplinary multi-university centers including SRC/JUMP ComSenTer and CONIX, and NSF SpectrumX. Prof. Cabric was a recipient of the Samueli Fellowship in 2008, the Okawa Foundation Research Grant in 2009, Hellman Fellowship in 2012, the National Science Foundation Faculty Early Career Development (CAREER) Award in 2012, and Qualcomm Faculty Awards in 2020 and 2021. Prof. Cabric is an IEEE Fellow.

Abstract: Reconfigurable intelligent surface (RIS) has gained much traction due to its potential to manipulate the propagation environment via nearly-passive reconfigurable elements. Attention has been drawn to the use of RIS 1.0 architectures based on diagonal phase shift matrices where each element of the RIS is connected to a load disconnected from the other elements. This enables simple RIS architectures to control the phase of the impinging wave and reflect the wave in the desired direction. This lecture argues that to truly exploit the benefits of RIS in 6G, RIS need to explore architectures beyond conventional diagonal phase shift matrices. This lecture bridges microwave theory and RIS communications, introduces the audience to Beyond Diagonal (BD) RIS, and show the benefits of BD RIS architectures in terms of controlling both phases and magnitudes of reflected waves (hence, high flexibility in wave manipulation), enabling simultaneous transmission and reflection, increasing reflected power, boosting sum-rate and flexibility in various deployments, enabling highly directional full-space wireless coverage, etc.

Bio: Bruno Clerckx is a (Full) Professor, the Head of the Wireless Communications and Signal Processing Lab, and the Deputy Head of the Communications and Signal Processing Group, within the Electrical and Electronic Engineering Department, Imperial College London, London, U.K. He is also the Chief Technology Officer (CTO) of Silicon Austria Labs (SAL) where he is responsible for all research areas of Austria''s top research center for electronic based systems. He received the MSc and Ph.D. degrees in Electrical Engineering from Université Catholique de Louvain, Belgium, and the Doctor of Science (DSc) degree from Imperial College London, U.K. Prior to joining Imperial College in 2011, he was with Samsung Electronics, Suwon, South Korea, where he actively contributed to 4G (3GPP LTE/LTE-A and IEEE 802.16m). He has authored two books on “MIMO Wireless Communications” and “MIMO Wireless Networks”, 250 peer-reviewed international research papers, and 150 standards contributions, and is the inventor of 80 issued or pending patents among which 15 have been adopted in the specifications of 4G standards and are used by billions of devices worldwide. His research spans the general area of wireless communications and signal processing for wireless networks. He received the prestigious Blondel Medal 2021 from France for exceptional work contributing to the progress of Science and Electrical and Electronic Industries, the 2021 Adolphe Wetrems Prize in mathematical and physical sciences from Royal Academy of Belgium, multiple awards from Samsung, IEEE best student paper award, and the EURASIP (European Association for Signal Processing) best paper award 2022. He is a Fellow of the IEEE and the IET, and an IEEE Communications Society Distinguished Lecturer.

Abstract: Modern society is witnessing the emergence of complex networked systems driven by exchanges of information among their elements, such as robotic swarms, autonomous systems, social networks, and Internet-of-Things (IoT) architectures. In these applications, data is often collected from heterogeneous sources and from dispersed locations. It becomes imperative to design learning machines and decision-making algorithms that are better suited to the reality of networked units. The methodologies will need to account for coupling among intelligent agents in a manner that enables multi-tasking and is robust to interference. This talk presents a framework for decentralized learning from networked data, which we refer to as Social Machine Learning. This approach handles heterogeneity in data more gracefully than existing methods, learns with performance guarantees, is more resilient to adversarial attacks, and promotes explainable and fair learning. By relying on social interactions among agents, the architecture is also infused with a higher level of robustness to manipulation. This is because it is more difficult to fool a group of agents than an individual agent.

Bio: A. H. Sayed serves as Dean of Engineering at EPFL, Switzerland, where he also directs the Adaptive Systems Laboratory (https://asl.epfl.ch/). He has served before as Distinguished Professor and Chair of Electrical Engineering at UCLA. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS). He served as President of the IEEE Signal Processing Society in 2018 and 2019. An author of over 600 scholarly publications and 9 books, including most recently the 3-volume treatise on Inference and Learning from Data, published by Cambridge University Press in 2022. His research involves several areas including adaptation and learning theories, data and network sciences, statistical inference, and information processing theories. His work has been recognized with several major awards including more recently the 2022 IEEE Fourier Technical Field Award and the 2020 IEEE Wiener Society Award.

Abstract: In this talk, we will discuss the use of deep learning in statistical signal processing. We will address settings in which the classical solutions are intractable and will propose modern approaches based on neural networks. We will begin with parameter estimation and focus on learning non-linear minimum variance unbiased estimators (MVUE). Next, we will switch to detection theory and focus on learning classifiers with constant false alarm rates (CFAR). In both settings, we provide deep learning methods that achieve these goals in practice, as well as theory that highlights the relations to the classical likelihood-based solutions.

Bio: Ami Wiesel received the B.Sc. and M.Sc. degrees in Electrical Engineering from Tel-Aviv University, Tel-Aviv, Israel, in 2000 and 2002, respectively, and the Ph.D. degree in Electrical Engineering from the Technion - Israel Institute of Technology, Haifa, Israel, in 2007. He was a postdoctoral fellow in the University of Michigan, Ann Arbor, USA, during 2007–2009. He is currently a Professor in the Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University of Jerusalem, Israel.