Thematic Talks
TT1 – Shuguang Cui, Future Network of Intelligence Institute (FNii) at CUHK, Shenzhen
Title: The Merging between AI and Wireless Communication
Presenter:
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Dr. Shuguang Cui |
Biography: Abstract: AI and communication network happily meet in this era. On one hand, AI could enable various new network optimization and control features, which were not feasible with traditional network control approaches. Many people believe AI will be the core or brain of next generation networks. On the other hand, the future AI systems will become more complex, and inevitably distributed. To boost the performance of such distributed AI systems, the network connection among the scattered intelligent elements must be optimized. Understanding such two-way dynamics between AI and networks will be a key step towards future information systems. In this talk, we will explore the principles regulating the synergy between AI and wireless communication, and share some recent progresses in this exciting area.
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TT2 – Petros Elia, Department of Communication Systems at EURECOM in Sophia Antipolis, France
Title: Multi-User Distributed Computing and the Deep Connections with Coding Theory, Compressed Sensing, and Tessellation.
Presenter:
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Dr. Petros Elia |
Biography: Petros Elia received the B.Sc. degree from the Illinois Institute of Technology, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Southern California (USC), Los Angeles, in 2001 and 2006 respectively. He is now a professor with the Department of Communication Systems at EURECOM in Sophia Antipolis, France. His latest research deals with the intersection of coded caching and feedback-aided communications in multiuser settings. He has also worked in the area of complexity-constrained communications, MIMO, queueing theory and cross-layer design, coding theory, information theoretic limits in cooperative communications, and surveillance networks. He is a Fulbright scholar, the co-recipient of the NEWCOM++ distinguished achievement award 2008-2011 for a sequence of publications on the topic of complexity in wireless communications, the recipient of the ERC Consolidator Grant 2017-2022 on cache-aided wireless communications, and the recipient of the ERC-PoC 2022-2024. Abstract: In this work, we investigate the problem of multi-user distributed computing, where various servers help compute the desired linearly separable (but generally non-linear) functions of various users. We explore the classical problem of the tradeoff between computation and communication cost, and we establish novel relationships with coding theory and compressed sensing and tessellation theory.
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TT3 – Robert Heath, Department of ECE at North Carolina State University
Title: Signal and array processing with an electromagnetic array manifold
Presenter:
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Dr. Robert Heath |
Biography: Robert W. Heath Jr. is the Lampe Distinguished Professor in the Department of ECE at North Carolina State University. He is the recipient or co-recipient of several awards including the 2019 IEEE Kiyo Tomiyasu Award, the 2020 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, the 2020 North Carolina State University Innovator of the Year Award and the 2021 IEEE Vehicular Technology Society James Evans Avant Garde Award. He authored "Introduction to Wireless Digital Communication” (Prentice Hall in 2017) and "Digital Wireless Communication: Physical Layer Exploration Lab Using the NI USRP” (National Technology and Science Press in 2012). He co-authored “Millimeter Wave Wireless Communications” (Prentice Hall in 2014) and "Foundations of MIMO Communications" (Cambridge 2019). He was a member-at-large of the IEEE Communications Society Board-of-Governors (2020-2022) and a member-at-large on the IEEE Signal Processing Society Board-of-Governors (2016-2018). He was EIC of IEEE Signal Processing Magazine from 2018-2020. He is a licensed Amateur Radio Operator, a registered Professional Engineer in Texas, a Private Pilot, a Fellow of the National Academy of Inventors, and a Fellow of the IEEE. Abstract: The array manifold is a fundamental tool in wireless communication that characterizes how arrays respond to different excitations. In this presentation, we present an electromagnetic-based array manifold that represents antennas as groups of Hertzian dipoles. We show how this characterization leverages the array current distribution to account for polarization, mutual coupling, and even near-field effects. We compare our proposed manifold with traditional models, such as the isotropic and embedded manifolds, and highlight situations in which our approach achieves significant benefits. We conclude by showing applications of the proposed manifold to beam pattern synthesis. We find that the electromagnetic manifold can be used to beamform with arbitrary antennas and array configurations. This is joint work with Dr. M. Rodrigo Castellanos.
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TT4 – Meixia Tao, Shanghai Jiao Tong University, China
Title: Federated Edge Learning: Communication-Efficient Designs and Applications in Wireless Networks
Presenter:
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Dr. Meixia Tao |
Biography: Meixia Tao (F'19) is a Professor with the Department of Electronic Engineering, Shanghai Jiao Tong University, China. She received the B.S. degree in electronic engineering from Fudan University, Shanghai, China, in 1999, and the Ph.D. degree in electrical and electronic engineering from Hong Kong University of Science and Technology in 2003. Her current research interests include wireless edge learning, coded caching, reconfigurable intelligence surfaces, and semantic communications. She receives the 2019 IEEE Marconi Prize Paper Award, the 2013 IEEE Heinrich Hertz Award for Best Communications Letters, the IEEE/CIC International Conference on Communications in China (ICCC) 2015 Best Paper Award, and the International Conference on Wireless Communications and Signal Processing (WCSP) 2012 and 2022 Best Paper Awards. She also receives the 2009 IEEE ComSoc Asia-Pacific Outstanding Young Researcher award. Dr. Tao is an Associate Editor of the textsc{IEEE Transactions on Information Theory} and an Editor-at-Large of the textsc{IEEE Open Journal of the Communications Society}. She served as a member of the Executive Editorial Committee of the textsc{IEEE Transactions on Wireless Communications} during 2015-2019. She was also on the Editorial Board of several other journals as Editor or Guest Editor, including the textsc{IEEE Transactions on Communications} and textsc{IEEE Journal on Selected Areas in Communications}. She also served as the TPC Co-Chair of IEEE ICC 2023. Abstract: Traditional artificial intelligence (AI) applications deployed in cloud data centers require extensive data acquisition, transmission, and processing, causing significant challenges in latency, energy, and privacy. FEderated Edge Learning (FEEL) emerges as a disruptive learning framework to address these issues by leveraging the sensing, computation, and communication capabilities at the network edge. FEEL allows collaborative training of global AI models across geographically distributed edge devices without accessing local private datasets by exchanging only model parameters. FEEL facilitates many emerging intelligent edge services promised by 6G, such as autonomous driving, and immersive communications. Despite its advantages, FEEL faces several key challenges, such as limited on-device computation capacities, heterogenous data distribution, and scarce radio resources. This talk will present our recent research progress towards communication-efficient and high-performance FEEL, covering topics like fundamental limits of communication efficiency, over-the-air model aggregation, federated multi-task learning, and federated knowledge distillation. Applications of FEEL for the design and optimization of wireless communication networks, including wireless D2D network power control and cell-free massive MIMO precoding, will also be discussed.
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TT5 – Jing Han, Northwestern Polytechnical University, China
Title: Orthogonal Signal-Division Multiplexing (OSDM) for Underwater Acoustic Communications
Presenter:
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Dr. Jing Han |
Biography: Jing Han received the B.Sc. degree in electrical engineering, the M.Sc. and Ph.D. degrees in signal and information processing from Northwestern Polytechnical University, Xi’an, China, in 2000, 2003 and 2008, respectively. He is now a Professor at the School of Marine Science and Technology, Northwestern Polytechnical University. From June 2015 till June 2016, he was a visiting researcher at the Faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology, The Netherlands. His research interests include wireless communications, statistical signal processing, and particularly their applications to underwater acoustic systems. He is an Associate Editor of the EURASIP Signal Processing. Abstract: Underwater acoustic (UWA) channels are considered as one of the most challenging communication media in use. To achieve reliable transmission with high bandwidth efficiency over UWA channels, two low-complexity techniques, namely, orthogonal frequency-division multiplexing (OFDM) and single-carrier frequency domain equalization (SC-FDE), have received much attention in recent years. Both schemes are based on block-wise frequency-domain processing, which allows for mitigating the channel frequency selectivity more efficiently. However, it is well known that OFDM systems suffer from a large peak-to-average power ratio (PAPR) and a high sensitivity to Doppler effects. On the other hand, the SC-FDE system offers lower PAPR and better Doppler tolerance, yet at the expense of an inflexible bandwidth and energy management. As another promising alternative, orthogonal signal-division multiplexing (OSDM) is a new modulation scheme which provides a generalized framework to unify OFDM and SC-FDE. In this talk, we will focus on comparing the characteristics between OSDM and other existing modulation schemes, and introduce recent advances of its application in UWA communications.
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TT6 – Mugen Peng, Beijing University of Posts & Telecommunication (BUPT), China
Title: Integrated sensing and communication systems: Performance Analysis and Key Techniques
Presenter:
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Dr. Mugen Peng |
Biography: Mugen Peng is a professor in Beijing University of Posts & Telecommunication (BUPT), He serves as the Dean of the School of Information and Communication Engineering, and the Deputy Director of the State Key Laboratory of Networking and Switching Technology. His current research topics include wireless communication theory, network intelligence and non-terrestrial networks. Prof. Peng is serving or has been served on the Editorial/Associate Editorial Board of the IEEE Commun. Mag., IEEE IoT Journal, IEEE TVT, IEEE Network, and several other journals. He received the 2018 Heinrich Hertz Prize Paper Award, 2014 IEEE ComSoc AP Outstanding Young Researcher Award, and Best Paper Award in the IEEE ICC 2022, JCN 2016, IEEE WCNC 2015, IEEE GameNets 2014, IEEE CIT 2014, ICCTA 2011, IC-BNMT 2010, and IET CCWMC 2009. Abstract: The emergence of 6G intelligent services with multi-dimensional and stringent performance requirements necessitates the integration of sensing and communication (ISAC). Compared to dedicated communication or sensing solutions, ISAC is promising to achieve advantages in terms of cost, power consumption, and hardware size, while fostering potential mutual gains. However, the studies on communication and sensing have historically been conducted independently and their interactions remain unclear. This report introduces and analyzes the performance of ISAC systems in aspects of various waveforms and frequencies. Specifically, the pilot-assisted communication and delay-Doppler channel calibration is developed and explained theoretically, separately leading to expanded ISAC performance limits for orthogonal frequency division multiplexing (OFDM) and orthogonal time-frequency apace (OTFS). The trade-off and performance limits of ISAC for multiple frequencies are given individually, characterized by various critical technologies in the WIFI band, millimeter wave band, and terahertz band. Meanwhile, the key techniques in ISAC systems are presented as well.
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TT7 – Shi Jin, Southeast University, China
Title: Facilitating AI-based CSI Feedback Deployment in Massive MIMO Systems with Learngene
Presenter:
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Dr. Shi Jin |
Biography: Shi Jin received the B.S. degree in communications engineering from Guilin University of Electronic Technology, Guilin, China, in 1996, the M.S. degree from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2003, and the Ph.D. degree in information and communications engineering from Southeast University, Nanjing, in 2007. From June 2007 to October 2009, he was a Research Fellow at the Adastral Park Research Campus, University College London, London, U.K. He is currently affiliated with the faculty of the National Mobile Communications Research Laboratory, Southeast University. His research interests include wireless communications, random matrix theory, and information theory. He serves as an Area Editor for the IEEE Transactions on Communications and IET Electronics Letters. He was previously an Associate Editor for the IEEE Transactions on Wireless Communications, IEEE Communications Letters, and IET Communications. Dr. Jin and his co-authors were awarded the 2011 IEEE Communications Society Stephen O. Rice Prize Paper Award in the field of communication theory, a 2022 Best Paper Award, and a 2010 Young Author Best Paper Award by the IEEE Signal Processing Society. Abstract: Transfer learning presents a promising approach for enabling intelligent channel state information (CSI) feedback to adapt to dynamic scenarios in practical deployments. However, implementing this approach requires a pre-trained model, which must be trained with massive CSI samples and supported by a public neural network (NN) architecture. A more practical solution is to distribute the pre-trained model to the base station (BS) instead of directly providing CSI datasets. Nevertheless, the varying architectures across BS manufacturers pose a challenge that prevents this distribution. In this talk, we introduce a novel resource distribution framework called “CSI meta-knowledge support.” This framework facilitates the economical and effective distribution and utilization of CSI meta-knowledge among different BS manufacturers, achieving a better tradeoff between utility, privacy, and communication. The proposed scheme leverages Learngene to transfer and utilize CSI meta-knowledge in heterogeneous NN architectures. We design a Learngene unit that carries sufficient meta-knowledge, serving as a CSI learning ware provided by the platform. This enables manufacturers to incorporate the platform-provided meta-knowledge while meeting their design criteria and BS requirements, allowing them to expand into their proprietary models. In the experimental results, our method demonstrates an approximate 4 dB performance improvement, a 3/4 reduction in the required training sample amount, and faster convergence compared to the baseline approach.
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TT8 – Hong Zheng, Eigencomm
Title: Cellular-loT Evolution and Key Performance Challenge
Presenter:
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Dr. Hong Zheng |
Biography: Hong Zheng is the vice president of R&D, at Eigencomm. She is leading Eigencomm LTE Cat.1 and 5G redcap research and development. Prior to joining Eigencomm, Hong was the senior director of System design and Protocol Stack team at Marvell and worked on TD-SCDMA and LTE modem projects. She has 10+ years of design experience in cellular terminal solutions covering the technologies from legacy 2G/3G to current 4G and 5G. Abstract： Cellular-IoT is 3gpp-based ecosystem and has been widely adopted in the world. In the speech, we will introduce the Cellular- IoT technology roadmap from 2G to 5G, the expansion in various industry fields and future evolution. We will also talk about key performance challenges for C-IoT chipset design in terms of ultra-high integration, ultra-small size and ultra-low power consumption. We will compare the main technical characteristics of the three main C-IOT techniques: NB-IoT, Cat.1bis and 5G Redcap, explain the advantage and constraints for each one, describe the typical use cases, and analyze the industry trends in the future. We will also talk about two critical performance challenges for C-IoT chipset design - high integration and low cost, and how to achieve these goals by multiple approaches - SoC, IPD, hardware accelerators and advanced RF &Analog design. |
TT9 – Hong Zhou, Institute of Strategic Research, Huawei, China
Title: Challenges of Wireless Communications
Presenter:
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Dr. Hong Zhou |
Biography: Hong Zhou is the President of the Institute of Strategic Research, Huawei, and the Director of the National Key Laboratory of Wireless Broadband Communication Systems. Dr. Zhou joined Huawei in 1997 and has served as Chief of the Shanghai Research Center, Vice President of the Wireless Network Product Line, President of the Central Hardware Engineering Institute, and President of the European Research Institute. In these roles, Dr. Zhou has been responsible for research, standardization, industrialization, and technical cooperation activities of the related business. Abstract: In recent years, 5G has made great progress in improving network capacity and user experience, and enabling industry digitalization to improve service quality and network efficiency. At the same time, we also face some important challenges: First, how to help operators improve their profitability; Second, how to reduce energy cost of wireless systems; Third, how to improve user experience at the cell edge. Therefore, on the basis of re-thinking about the assumptions and usage scenarios with the Shannon's era, we envision new discoveries, new understandings and new usage scenarios in the post-Shannon era. We propose to solve the above practical problems by using the world's a-prior knowledge, by using new modes and beams of electromagnetic fields, and by developing narrow beam technology in urban environments. Finally, we raise some questions about some of the current hot technology trends, hoping that the academic community will actively think about the implementability of novel technologies within the business vision.
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