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Time: 22 May 2026

Location: Room 611, EEE Building, South Kensington Campus, 911½ñÈÕºÚÁÏ

Talk Schedule

Participating Scholars

Talk Content

Title: Sub-THz Wavefront Engineering for Near-field Communications Beyond 6G: From Theory to Experiments

Speaker: Doohwan Lee

Abstract: As 6G networks evolve towards higher frequency bands including sub-terahertz (sub-THz) and centimeter-wave spectrum, the shortened wavelengths enable exploitation of physical properties of electromagnetic waves that have remained largely underutilized in conventional wireless systems. This tutorial introduces the revolutionary concept of ``multishape radio,'' which leverages unique electromagnetic wave characteristics such as non-diffracting propagation, orbital angular momentum (OAM), and curved main lobe propagation to enhance wireless communication performance beyond the limitations of traditional plane-wave beamforming. The manipulation of sub-THz electromagnetic waves using reconfigurable intelligent surfaces (RIS) enables more customized beam control and the application of Airy and Bessel beams, which have not been extensively utilized until now. This tutorial provides comprehensive coverage of three key multishape radio technologies: Bessel beams for non-diffracting and self-healing transmission, OAM beams for high-capacity spatial multiplexing, and Airy beams for interference-free communications with curved main lobe propagation characteristics. The tutorial bridges theoretical foundations with experimental validation, featuring real-world demonstrations in sub-THz bands that showcase breakthrough achievements including terabit-class wireless transmission (1.58 Tb/s OAM multiplexing), enhanced coverage through self-healing propagation (15.9 dB power improvement with Bessel beams), and interference-free multi-stream communications (949.34 Gb/s with 8 Airy beams). Attendees will gain practical insights into lens design, beam generation techniques, and system implementation strategies that enable next-generation wireless networks to harness the full potential of electromagnetic wave physics. This tutorial will provide the audience with an integrated understanding of the latest sub-THz experimental systems, practical beam generation strategies, and pathways for deployment in 6G network architecture.

Bio: Dr. Doohwan Lee is a Senior Distinguished Researcher at NTT Network Innovation Laboratories. From 2012 to 2014, he was a full-time lecturer at the the University of Tokyo. From 2016 to 2018, he was a part-time lecturer at Kanagawa University. He received the Best Paper Award and Best Technical Exhibition Award from the IEICE Software Radio in 2011, the IEICE Communications Society Excellent Paper Award in 2012, the IEICE SRW Young Researcher’s Award in 2016, the Best Technical Exhibition Award from SmartCom 2014, the Best Paper Award from SmartCom 2016, the Best Paper Award and Special Technical Award in Smart Radio from IEICE Smart Radio in 2017, the Distinguished Contributions Award from the IEICE Communications Society in 2021, the ComEX Best Letter Award in 2023, and the Radio Achievement Award from the Ministry of Internal Affairs and Communications in Japan in 2024. He has served as general co-chair of many workshops held at the IEEE ICC, GLOBECOM, PIMRC, and VTC. He was a co-chair of the Membership Development Committee (MDC) of the IEEE ComSoC APB in 2022 and 2023.

Title: Fundamental Limit of CSI Compression in FDD Massive MIMO

Speaker: Namyoon Lee

Abstract: Fundamental Limits of CSI Compression in FDD Massive MIMO Channel state information (CSI) feedback in frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems is fundamentally limited by the high dimensionality of wideband channels. Classical transform coding (TC) provides an optimal solution when the CSI follows a single correlated Gaussian distribution. In practice, however, CSI datasets exhibit multiple propagation regimes arising from variations in user location, scattering geometry, and blockage. As a result, a single covariance model is often mismatched to the true channel statistics. In this paper, we model the stacked wideband CSI vector as a Gaussian-mixture source with a latent geometry state that represents different propagation environments. Each component corresponds to a locally stationary regime characterized by a correlated proper complex Gaussian distribution with its own covariance matrix. This representation captures the multimodal nature of practical CSI datasets while preserving the analytical tractability of Gaussian models. Motivated by this structure, we propose Gaussian-mixture transform coding (GMTC), a practical CSI feedback architecture that combines state inference with state-adaptive TC. The mixture parameters are learned offline from channel samples and stored as a shared statistical dictionary at both the user equipment (UE) and the base station. For each CSI realization, the UE identifies the most likely geometry state, encodes the corresponding label using a lossless source code, and compresses the CSI using the Karhunen–Loève transform matched to that state. We further characterize the fundamental limits of CSI compression under this model by deriving analytical converse and achievability bounds on the rate–distortion (RD) function. A key structural result is that the optimal bit allocation across all mixture components is governed by a single global reverse-waterfilling level. Simulations on the COST2100 dataset show that GMTC significantly improves the RD tradeoff relative to neural transform coding approaches while requiring substantially smaller model memory and lower inference complexity. These results indicate that near-optimal CSI compression can be achieved through state-adaptive TC without relying on large neural encoders.

Bio: Namyoon Lee received the B.E. degree from Korea University, Seoul, South Korea, in 2006, the M.S. degree from KAIST, Daejeon, South Korea, in 2008, and the Ph.D. degree in electrical and computer engineering from The University of Texas at Austin, Austin, TX, USA, in 2014. He gained valuable industry experience at the Samsung Advanced Institute of Technology from 2008 to 2011, Nokia Research Center, Berkeley, from 2014 to 2015, and Intel Labs, Santa Clara, from 2015 to 2016. Since 2016, he has been a Professor with the Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, South Korea. He has contributed extensively to the field through numerous publications, patents, and collaborative research with industry. His research interests include communication theory, with emphasis on multi-antenna communication, error correction codes, and machine-learning-aided communication systems. He was a recipient of several prestigious honors, including the 2016 IEEE ComSoc Asia–Pacific Outstanding Young Researcher Award, the 2020 IEEE Best Young Professional Award (Outstanding Nominee), and the 2021 IEEE–IEIE Joint Award for Young Scientist and Engineer.

Title: Rydberg Quantum Receiver: The Next Frontier of Wireless Communication and Sensing

Speaker: Kaibin Huang

Abstract: The advancement of Rydberg atoms in quantum information technology is driving a paradigm shift from classical receivers to Rydberg atomic receivers (RARE). RAREs utilize the electron transition phenomenon of highly-excited atoms to interact with electromagnetic (EM) waves, thereby enabling the detection of wireless signals. Operating at the quantum scale, such new receivers have the potential to breakthrough the sensitivity limit of classical receivers, sparking a revolution in wireless communications. In this talk, I will first introduce the fundamentals of RAREs, covering their definition and properties, the interaction of Rydberg atoms with EM waves, as well as the electromagnetically-induced-transparency based quantum measurement. Then, the pros and cons of RAREs compared as opposed to classical receivers will be discussed. The second part of this talk will present our latest progress in RARE aided communications and sensing, ranging from MIMO communications, sensing architecture, and integrated communication and sensing. The talk will be concluded with some promising future directions on integration of RARE into modern wireless communication systems.

Bio: Kaibin Huang received the B.Eng. and M.Eng. degrees from the National University of Singapore and the Ph.D. degree from The University of Texas at Austin, all in electrical engineering. He is the Philip K H Wong Wilson K L Wong Professor in Electrical Engineering and the Department Head at the Dept. of Electrical and Electronic Engineering, The University of Hong Kong (HKU), Hong Kong. His work was recognized with seven Best Paper awards from the IEEE Communication Society. He is a member of the Engineering Panel of Hong Kong Research Grants Council (RGC) and a RGC Research Fellow (2021 Class). He has served on the editorial boards of five major journals in the area of wireless communications and co-edited ten journal special issues. He has been active in organizing international conferences such as the 2014, 2017, and 2023 editions of IEEE Globecom, a flagship conference in communication society. He has been named as a Highly Cited Researcher by Clarivate in the last six years (2019-2024) and an AI 2000 Most Influential Scholar (Top 30 in Internet of Things) in 2023-2024. He was an IEEE Distinguished Lecturer. He is a Fellow of the IEEE and the U.S. National Academy of Inventors.

Title: A Pulse-Shaped OFDM Framework and Exact I/O Analysis for AFDM

Speaker: Hai Lin

Abstract: As a promising chirp-domain waveform for high-mobility communications, affine frequency division multiplexing (AFDM) has been largely studied through discrete-sequence models. This talk revisits AFDM from its continuous-time foundations. First, we show that the ideal AFDM waveform falls within the conventional Weyl-Heisenberg framework for multicarrier modulation, with the root chirp serving as a constant-envelope prototype pulse, allowing it to be interpreted as a pulse-shaped OFDM signal. This perspective enables analytical PSD characterization and explains why practical implementations adopt sub-Nyquist samples, leading to aliased waveforms. Second, for the practically implemented pulse-shaped (PS) AFDM with approximate aliased chirps, we derive an exact input-output relation over delay-Doppler channels by accounting for the receive filtering required in any physical receiver, revealing a structural difference from the idealized relation commonly used in the literature. Simulation results validate the analysis.

Bio: Hai Lin received the B.E. degree from Shanghai Jiao Tong University, China, in 1993, the M.E. degree from the University of the Ryukyus, Japan, in 2000, and the Dr.Eng. degree from Osaka Prefecture University, Japan, in 2005. In 2000, he joined Osaka Prefecture University (renamed Osaka Metropolitan University in 2022), where he is currently a Professor. He received the IEEE GLOBECOM Best Paper Award in 2018 and 2023, and the IEEE ComSoc Asia-Pacific Outstanding Paper Award in 2025. He has served as a Technical Program Co-Chair for symposia and tracks at major international conferences, including IEEE ICC, GLOBECOM, WCNC, and VTC. He served as Chair of the SPCE TC of the IEEE Communications Society from 2015 to 2016, and as Chair of the IEEE Communications Society Kansai Chapter from 2022 to 2023. He previously served as an Associate Editor for the IEEE Transactions on Wireless Communications and is currently an Associate Editor for the IEEE Transactions on Communications and the IEEE Transactions on Vehicular Technology.

Title: Learning Radio Maps via Graph Transformer for User-Centric Cell-Free Massive MIMO

Speaker: Wei Zhang

Abstract: This talk addresses the challenge of resource allocation in user-centric cell-free massive MIMO systems. Conventional methods rely heavily on instantaneous channel state information, which requires real-time pilot measurements and generates substantial backhaul overhead. To overcome this limitation, we construct a radio map as a network surrogate that predicts the expected downlink rates of user equipment from their positions, under dynamic access point selection and power allocation strategies. Our technical contributions are threefold. First, we target rate prediction by adopting a divide-and-conquer strategy that decomposes rate prediction into signal and interference power prediction. Second, we employ a graph-based representation where access points and users form a bipartite graph, naturally capturing dynamic associations and adapting to varying network configurations. Third, we introduce a Transformer-based multi-head attention mechanism to accurately model the heterogeneous influences of neighboring nodes on the target user. Simulations on the DeepMIMO dataset demonstrate that our radio map significantly outperforms state-of-the-art methods in rate prediction accuracy across various signal-to-noise ratio regimes and user densities, providing an interpretable, scalable surrogate for predictive resource allocation.

Bio: Wei Zhang (F'15) is a professor at the University of New South Wales, Sydney, Australia. He was Vice President of IEEE Communications Society between 2022 and 2025. His research interests include 6G communications and networks. He has been an IEEE Fellow since 2015 and was an IEEE ComSoc Distinguished Lecturer in 2016-2017. Within the IEEE ComSoc, he has taken many leadership positions including Chair of Wireless Communications Technical Committee (2019-2020), Vice Director of Asia Pacific Board (2016-2021), Editor-in-Chief of IEEE Wireless Communications Letters (2016-2019), Member-at-Large on the Board of Governors (2018-2020), Technical Program Committee Chair of APCC 2017 and ICCC 2019 and 2024, Award Committee Chair of Asia Pacific Board and Award Committee Chair of Technical Committee on Cognitive Networks. He received the IEEE Communications Society Joseph LoCicero Award in 2024. He obtained the Ph.D. degree from the Chinese University of Hong Kong in 2005.

Title: Sensing With Random Communication Signals

Speaker: Fan Liu

Abstract: To maximize the efficiency of wireless resource utilization, 6G integrated sensing and communication (ISAC) systems must exploit the inherently random communication data payloads to serve both sensing and communication functions. This talk provides a comprehensive technical overview of signal processing methodologies for sensing with random communication signals. We begin by revisiting the deterministic-random tradeoff (DRT), highlighting the necessity for specialized processing techniques tailored to random ISAC signals. Building upon this foundation, we review the core signal models and processing pipelines underpinning communication-centric ISAC systems. A particular focus is given to analyzing the average squared auto-correlation function (ACF) of random ISAC signals, a key performance metric for multi-target ranging tasks. Drawing insights from these theoretical results, we outline the design principles for three critical components: modulation schemes, constellation design, and pulse shaping filters. The overarching goal is to enhance sensing capability without compromising communication efficiency, or to enable a scalable tradeoff between the two. The talk concludes by highlighting open challenges and future research directions for advancing sensing with communication signals in 6G-era networks.

Bio: Fan Liu is currently a Professor with the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China. Prior to that, he was an Assistant Professor with the Southern University of Science and Technology, Shenzhen, China, from 2020 to 2024. He received the Ph.D. and the BEng. degrees from Beijing Institute of Technology (BIT), Beijing, China, in 2018 and 2013, respectively. He has previously held academic positions in the University College London (UCL), London, UK, as a Visiting Researcher from 2016 to 2018, and a Marie Curie Research Fellow from 2018 to 2020. Prof. Liu's research interests lie in the general area of signal processing and wireless communications, and in particular in the area of Integrated Sensing and Communications (ISAC). He is the founding Chair of the IEEE ComSoc ISAC Emerging Technology Initiative (ISAC-ETI), Chair and founding member of the IEEE SPS ISAC Technical Working Group (ISAC-TWG), an elected member of the IEEE SPS Sensor Array and Multichannel Technical Committee (SAM-TC), an Associate Editor of the IEEE Transactions on Communications, IEEE Transactions on Mobile Computing, and IEEE Open Journal of Signal Processing, a Guest Editor of the IEEE Journal on Selected Areas in Communications, IEEE Wireless Communications, and IEEE Vehicular Technology Magazine, and a Moderator of the ArXiv EESS area. He was a TPC Co-Chair of the inaugural edition of the IEEE ISAC Conference 2026 and the 2nd-4th IEEE Joint Communication and Sensing (JC\&S) Symposium, a Symposium Co-Chair for the IEEE ICC 2026, IEEE/CIC ICCC 2025, IEEE WCNC 2024, and IEEE GLOBECOM 2023. He was listed among the 2025 Clarivate Highly Cited Researcher. He was a recipient of numerous Best Paper Awards, including the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, 2024 IEEE Signal Processing Society Best Paper Award, 2024 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, 2024 IEEE Communications Society Asia-Pacific Outstanding Paper Award, 2023 IEEE Communications Society Stephan O. Rice Prize, and 2021 IEEE SPS Young Author Best Paper Award. He is a Senior Member of the IEEE.

Title: Communication-efficient Federated Learning

Speaker: Xiangwei Zhou

Abstract: This talk presents recent advances in communication-efficient federated learning (FL) for large-scale and heterogeneous Internet of Things (IoT) networks. We first introduce a joint training and device scheduling framework with a novel Group Scheduling on OFDMA (GS-OFDMA) protocol to reduce communication latency and overall training cost under wireless and system heterogeneity. By jointly optimizing client participation, communication rounds, local training epochs, and transmission scheduling, the proposed framework significantly improves cost efficiency and convergence performance. We then present DualGFL, a dual-level game-theoretical FL framework that combines coalition formation and auction-based participation to simultaneously enhance both server and client utilities in cooperative-competitive environments. Finally, the talk discusses ongoing work on integrating Mixture-of-Experts (MoE) architectures into federated learning, focusing on intelligent client-expert alignment, load balancing, and capacity-aware training to enable scalable, communication-efficient, and personalized large AI models at the network edge.

Bio: Xiangwei Zhou received the Ph.D. degree in electrical and computer engineering from Georgia Institute of Technology, Atlanta, GA, USA, in 2011. He is a Professor with the Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, USA. His research interests include wireless communications and statistical signal processing, with a recent focus on federated learning, edge intelligence, and resilient spectrum sharing in next-generation wireless systems. Dr. Zhou was the recipient of the Best Paper Award at the 2014 International Conference on Wireless Communications and Signal Processing and served as an Editor for the IEEE Transactions on Wireless Communications from 2013 to 2018.