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2026-10-15 (15:00) : Seminar on Wireless Sensing and Communications

At Nyquist

Duration: 120 minutes

Organized by Electrical Engineering

Section 1: Optimal Sparse Array Design for Sensing and Communications (15:00)

Speaker : Marius Pesavento (TU Darmstadt)

Marius Pesavento

Marius Pesavento received the Dipl.-Ing. and Ph.D. degrees from Ruhr-Universität Bochum, Germany, in 1999 and 2005, respectively, and the M.Eng. degree from McMaster University, Hamilton, ON, Canada, in 2000. From 2005 to 2008, he was a Research Engineer with two start-up companies. He became an Assistant Professor in 2010 and a Full Professor (W3) of Communication Systems in 2013 in the Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Germany. His research interests include statistical and sensor array signal processing, MIMO communications, optimization, and model-assisted deep learning. He is the Editor-in-Chief (2026–2028) of the IEEE Open Journal of Signal Processing and has served on the Editorial Board of the IEEE Transactions on Signal Processing (2012-2016) and the EURASIP Journal on Signal Processing (2011-2025). He is a member of the “Signal Processing Theory and Methods” Technical Committee (since 2021), served as Vice-Chair of the IEEE SPS “Sensor Array and Multichannel Signal Processing” Technical Committee in 2025 (member, 2012-2017), and is Past Chair of the EURASIP “Signal Processing for Multisensor Systems” Technical Area Committee.

Abstract : Sparse linear arrays obtained by thinning large uniform linear arrays are widely used in wireless communications and radar sensing because they offer large effective apertures and high angular resolution with fewer sensors. However, thinning also introduces array ambiguities that can induce rank deficiencies in the associated channel or sensing matrix, thereby limiting spatial multiplexing gains and preventing unique multisource parameter estimation in certain scenarios. Conventional sparse-array designs for sensing are often based on local criteria, such as Fisher information matrix (FIM) analysis, which do not fully account for dependencies among sources. More fundamentally, the underlying problem is that of determining the spark of a generalized Vandermonde matrix – an important open question with connections to algebraic coding theory, polynomial approximation, and control theory. In this talk, we present a novel scalable framework for the design and analysis of sparse (thinned) uniform arrays. Specifically, we reformulate the problem as one of designing structured matrices of maximum rank, which in turn yields simple principles for constructing optimal sparse arrays. The resulting approach combines symbolic computation with structured rank optimization. It is simple, scalable, and well suited to the design of very large sparse arrays.

Section 2: Near-field Radio Localization and Mapping with Intelligent Surfaces (16:00)

Speaker : Anna Guerra (Università di Bologna)

Anna Guerra

Anna Guerra (Member, IEEE) received the Ph.D. degree in electronics, telecommunications, and information technologies from the University of Bologna, Italy, in 2016. From 2016 to 2021, she was a Postdoctoral Researcher with the University of Bologna. From 2018 to 2020, she received the Marie Skłodowska-Curie Global Fellowship for the H2020 AirSens Project and she spent the outgoing phase with Stony Brook University, Stony Brook, NY, USA. From 2021 to 2024, she was a Researcher with the National Research Council (CNR) of Italy. In 2023, she received the ERC Starting Grant for the Project CUE-GO. Since 2024, she has been an Associate Professor with the University of Bologna. She is an Associate Editor of IEEE Communications Letters, IEEE Wireless Communications Letters, and IEEE Transactions on Wireless Communications

Abstract : The next generation of cellular networks promises to revolutionize wireless communication by creating smart radio environments (SREs) through the use of reconfigurable intelligent surfaces (RISs). These RISs offer significant benefits, including the ability to optimize the radio channel for specific applications, thereby enhancing overall system performance. For instance, RIS elements can be reconfigured to achieve precise localization and sensing, while simultaneously improving communications in cellular networks. Additionally, with the adoption of large aperture arrays and high frequencies, the traditional plane wave approximation, which is valid in the far field, becomes inadequate. In these scenarios, cellular users are likely to be positioned in the near field relative to base stations and RISs. The resulting wavefront curvature allows for the extraction of both distance and angle-of-arrival information, enabling more accurate source localization with fewer deployed devices (single-anchor localization). This webinar will present recent advancements in near-field localization and sensing for next-generation communication systems. It will begin by exploring the localization fundamental localization limits in RIS-aided systems, followed by a review of algorithms designed for imaging and tracking mobile users. The webinar will also show how optimizing the phase profile of RISs can significantly impact localization performance.

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