Title: Spatial Sigma-Delta Modulation for Massive MIMO Downlink
Dr. Wing-Kin Ma
Wing-Kin Ma is currently a Professor with the Department of Electronic Engineering, Chinese University of Hong Kong (CUHK), Hong Kong. His research interests include signal processing, optimization, related topics in machine learning and data science, and communications. Some of his recent research includes symbol-level precoding and coarsely quantized MIMO transceiver designs.Dr. Ma has rich editorial experience, previously serving as editors of a number of important signal processing journals and in various capacities. He is now the Editor-in-Chief of IEEE Transactions on Signal Processing. He was a Tutorial Speaker at EUSIPCO 2011 and ICASSP 2014. He was the recipient of the CUHK Research Excellence Award 2013–2014, the 2015 IEEE Signal Processing Magazine Best Paper Award, the 2016 IEEE Signal Processing Letters Best Paper Award, and the 2018 IEEE Signal Processing Society (SPS) Best Paper Award. He was a TC member of two SPS technical committees, an IEEE SPS Distinguished Lecturer (2018—2019), and the SPS Regional Director-at-Large (2000—2021, Region 10).
Consider using a massive number of antennas at the base station to enhance system capabilities like spatial selectivity and spatial multiplexing gains. This sounds promising, but it comes with a price: significantly increased hardware cost and energy consumption over the RF front end. Recently there has been much interest in considering low-resolution ADCs/DACs for massive MIMO systems. The significant quantization effects, however, rise as the challenge. In this talk we will talk about a recently emerged alternative called spatial Sigma-Delta modulation. Sigma-Delta modulation is a classic signal processing concept that has been widely used in temporal ADCs/DACs. We will examine how Sigma-Delta modulation can be applied in space to mitigate quantization error effects in massive MIMO downlink, and how it brings a new perspective to MIMO precoding designs. If time permits, we will also cover another important topic, namely, symbol-level precoding, which provides a powerful solution to MIMO precoding designs under Sigma-Delta modulation.
Title: GPT Based Multi-User Communications
Presenter: Dr. Wen Tong, CTO, Wireless Network, Huawei Technologies Co., Ltd.
Dr. Wen Tong
Dr. Wen Tong is the CTO, Huawei Wireless and a Huawei Fellow. He is the head of Huawei wireless research, and the Huawei 5G chief scientist and led Huawei’s 10-year-long 5G wireless technologies research and development.
Prior to joining Huawei in 2009, Dr. Tong was the Nortel Fellow and head of the Network Technology Labs at Nortel. He joined the Wireless Technology Labs at Bell Northern Research in 1995 in Canada.
Dr. Tong is the industry recognized leader in invention of advanced wireless technologies, Dr. Tong was elected as a Huawei Fellow and an IEEE Fellow. He was the recipient of IEEE Communications Society Industry Innovation Award in 2014, and IEEE Communications Society Distinguished Industry Leader Award for “pioneering technical contributions and leadership in the mobile communications industry and innovation in 5G mobile communications technology” in 2018. He is also the recipient of R.A. Fessenden Medal. For the past three decades, he had pioneered fundamental technologies from 1G to 6G wireless with more than 530 awarded US patents.
Dr. Tong is a Fellow of Canadian Academy of Engineering, and he serves as Board of Director of Wi-Fi Alliance.
The new GPT (Generative pre-trained transformers) based communications paradigm is discussed, we further present the distributed GPT for wireless communications such as smartphone, robot and IoT devices. The GPT based the communications can enable the communications of “intelligence” rather than the information bits. Such a generalized framework can be more efficiently integrated into a physical world continuum platform, such as 6G ISAC (integrated sensing and communications). We exam the very large transformer as arithmetic information entropy from the Kolmogorov sense. Finally, in this talk, we also extend such a communications model into multi-user cases.
Title: Relevant information maximizing quantization with application in low resolution message passing decoding
Presenter: Gerhard Bauch, TU Hamburg
Dr. Gerhard Bauch
Gerhard Bauch received the Dipl.-Ing. and Dr.-Ing. degree in Electrical Engineering from Munich University of Technology (TUM) in 1995 and 2001, respectively, and the Diplom-Volkswirt (master in economics) degree from FernUniversitaet Hagen in 2001.
In 1996, he was with the German Aerospace Center (DLR), Oberpfaffenhofen, Germany. From 1996-2001 he was member of scientific staff at Munich University of Technology (TUM). In 1998 and 1999 he was also visiting researcher at AT&T Labs Research, Florham Park, NJ, USA. In 2002 he joined DOCOMO Euro-Labs, Munich, Germany, where he had been managing the Advanced Radio Transmission Group. In 2007 he was additionally appointed Research Fellow of DOCOMO Euro-Labs. He was a full professor at the Universität der Bundeswehr Munich from 2009-2012. Since October 2012 he is head of the Institute of Communications at Hamburg University of Technology.
He is a fellow of the IEEE and member of the board of governors of the IEEE Vehicular Technology Society, chair of the IEEE Communications Society German Chapter, member of the board of directors of the German Information Technology Society and member of the review board of the German Research Foundation.
Receiver sided signal processing particularly in high data rate and high performance applications causes bottleneck situations in terms of energy consumption, throughput, delay and chip area. Coarse quantization and low complexity operations are essential in order to address those issues. A popular recent trend is designing entire receiver chains, or some of their crucial building blocks from an information theoretical perspective. The framework of the Information Bottleneck Method provides a theoretical foundation as well as optimization tools for maximization of an appropriately defined relevant information that flows through the processing stages. We will discuss the principle of the Information Bottleneck Method and its applications to coarsely quantized signal processing with a focus on message passing decoding.
Title: Deep Learning for Future Wireless Communications
Presenter:Professor Geoffrey Li, Intelligent Transmission and Processing Lab, Imperial College London
Dr. Geoffrey Ye Li
Dr. Geoffrey Ye Li is currently a Chair Professor at Imperial College London, UK. Before joining Imperial in 2020, he was a Professor at Georgia Institute of Technology, USA, for 20 years and a Principal Technical Staff Member with AT&T Labs – Research (previous Bell Labs) in New Jersey, USA, for five years. He pioneered orthogonal frequency division multiplexing (OFDM) for wireless communications, established a framework on resource cooperation in wireless networks, and introduced deep learning (DL) for communications.
Dr. Geoffrey Ye Li was awarded IEEE Fellow and IET Fellow for his contributions to signal processing for wireless communications. He won 2024 IEEE Eric E. Sumner Award and several prestigious awards from IEEE Signal Processing, Vehicular Technology, and Communications Societies, including 2019 IEEE ComSoc Edwin Howard Armstrong Achievement Award.
Deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we will present recent work in DL in future wireless communications, including physical layer processing and resource allocation, DL-enabled semantic communications, impact of wireless communications on federated learning.
DL can improve the performance of each individual (traditional) block in a conventional communication system or jointly optimize the whole transceiver. We can categorize the applications of DL in physical layer processing into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model-drive DL for signal detection. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems.
Judicious resource (spectrum, power, etc.) allocation can significantly improve efficiency of wireless networks. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. Deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving and can help solve optimization problems for resource allocation or can be directly used for resource allocation. As an example, we will briefly discuss how to use deep reinforcement learning for wireless resource allocation in vehicular networks.
At the end of this talk, we will also briefly discuss semantic communications and impact of wireless communications on federated learning.
Title: When Bayes meets Kullback-Leibler: a Tale of Message Passing and Alternating Optimization
Presenter: Dirk Slock, EURECOM
Dr. Dirk Slock
Dirk T.M. Slock received an EE degree from Ghent University, Belgium in 1982. In 1984 he was awarded a Fulbright scholarship for Stanford University, USA, where he received the MSEE, MS in Statistics, and PhD in EE in 1986, 1989 and 1989 resp. While at Stanford, he developed new fast recursive least-squares algorithms for adaptive filtering. In 1989-91, he was a member of the research staff at the Philips Research Laboratory Belgium. In 1991, he joined EURECOM where he is now professor. At EURECOM, he teaches statistical signal processing (SSP) and signal processing techniques for wireless communications. He invented semi-blind channel estimation, the chip equalizer-correlator receiver used by 3G HSDPA mobile terminals, spatial multiplexing cyclic delay diversity (MIMO-CDD) now part of LTE, and his work led to the Single Antenna Interference Cancellation (SAIC) integrated in the GSM standard in 2006. Recent keywords are multi-cell multi-user (Massive) MIMO, imperfect CSIT, distributed resource allocation, variational and empirical Bayesian learning techniques, large random matrix analysis, audio source separation, location estimation and exploitation. He graduated about 40 PhD students, leading to an edited book and 500+ papers. In 1992 he received one best journal paper award from IEEE-SP and one from EURASIP. He is the coauthor of two IEEE Globecom'98, one IEEE SIU'04, one IEEE SPAWC'05, one IEEE WPNC’16 and one IEEE SPAWC’18 best student paper award, and a honorary mention (finalist in best student paper contest) at IEEE SSP'05, IWAENC'06, IEEE Asilomar'06 and IEEE ICASSP’17. He was an associate editor for the IEEE-SP Transactions in 1994-96 and the IEEE Signal Processing Letters in 2009-10. He was the General Chair of the IEEE-SP SPAWC'06 and IWAENC’14 workshops, and EUSIPCO’15. He cofounded the start-ups SigTone in 2000 (music signal processing products) and Nestwave in 2014 (Ultra Low-Power Indoor and Outdoor Mobile Positioning). He is a Fellow of IEEE and EURASIP. In 2018 he received the URSI France medal.
In the domains of communications and compressed sensing, the demand for effective approximate Bayesian estimation techniques is paramount. Sparse channel modeling extends traditional model selection, enabling optimized models based on available training data. Compressed sensing techniques extend Linear Minimum Mean Squared Error (LMMSE) estimation by a hierarchical Bayesian formulation. In multi-user detection or blind channel estimation, going beyond LMMSE and Gaussian models represents a leap.
One of the approaches in the realm of approximate Bayesian estimation is Variational Bayes (VB), a relatively straightforward method. VB can be seen as an extension of the Expectation-Maximization (EM) technique to scenarios involving random parameters, thereby yielding not only point estimates but also approximate posterior distributions. Notably, while VB yields accurate means in Gaussian problems, it tends to underestimate variances significantly.
An even more refined technique for approximate Bayesian estimation is Expectation Propagation (EP). Both VB and EP share the underlying concept of minimizing the Kullback-Leibler Divergence (KLD), albeit with different sequencing of the true and approximating probability density functions.
However, EP is a heuristic approach to minimizing a more desirable KLD, which is called the Bethe Free Energy (BFE). Exact alternating constrained minimization of the BFE leads to Belief Propagation (BP), whereas EP deviates from the alternating cost function and furthermore restricts approximating pdfs to be in an exponential family.
Taking a fresh look at alternating minimization of a KLD, the Central Limit Theorem leads to Gaussianity of the extrinsics in the marginal posteriors in moderate asymptotic settings. This in turn leads to what we call Gaussian Extrinsic Propagation, which sheds new light on characterizing performance beyond the loose Bayesian Cramer-Rao bound. Focusing on the Generalized Linear Model, assuming an i.i.d. (sign) statistical model for the measurement matrix allows asymptotically to find the variances without matrix inversions. This leads to Approximate Message Passing (AMP) in which the Onsager correction term w.r.t. Jacobi iterations for solving the normal equations for the mean is related to the Componentwise Conditionally Unbiased MMSE estimation. Reformulating AMP to correspond to alternating minimization of an asymptotic version of the BFE leads to a provably convergent algorithm. Alternating minimization becomes tricky in the presence of constraints and we shed some light on the desirable behavior of the Alternating Directions Method of Multipliers (ADMM) approach.
Extending the class of measurement matrices to Haar distributed unitary factors in the SVD allows to model more ill-conditioned problems, typically handled via the Vector AMP algorithm which assumes uniform variance profiles. To get correct individual variances is possible with a Unitary AMP in which the AMP variance predictions need to be corrected based on Haar Large System Analysis. We hope that this exploration of advances will inspire to extend the scope of problems tackled by these techniques, ultimately paving the way for enhanced Bayesian estimation methodologies.
Title: Resilience Through Cross-Technology Communication
Dr. Falko Dressler
Falko Dressler is full professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, TU Berlin. He received his M.Sc. and Ph.D. degrees from the Dept. of Computer Science, University of Erlangen in 1998 and 2003, respectively. Dr. Dressler has been associate editor-in-chief for IEEE Trans. on Mobile Computing and Elsevier Computer Communications as well as an editor for journals such as IEEE/ACM Trans. on Networking, IEEE Trans. on Network Science and Engineering, Elsevier Ad Hoc Networks, and Elsevier Nano Communication Networks. He has been chairing conferences such as IEEE INFOCOM, ACM MobiSys, ACM MobiHoc, IEEE VNC, IEEE GLOBECOM. He authored the textbooks Self-Organization in Sensor and Actor Networks published by Wiley & Sons and Vehicular Networking published by Cambridge University Press. He has been an IEEE Distinguished Lecturer as well as an ACM Distinguished Speaker. Dr. Dressler is an IEEE Fellow as well as an ACM Distinguished Member. He is a member of the German National Academy of Science and Engineering (acatech). He has been serving on the IEEE COMSOC Conference Council and the ACM SIGMOBILE Executive Committee. His research objectives include adaptive wireless networking (sub-6GHz, mmWave, visible light, molecular communication) and wireless-based sensing with applications in ad hoc and sensor networks, the Internet of Things, and Cyber-Physical Systems.
Co-existence of radio communication technologies has always been one of the key challenges in wireless communications. This particularly holds for listen-before-talk / carrier sensing based protocols. Coding, frequency hopping, and dynamic channel assignment techniques have been developed as mitigation strategies. Recently, co-existence has been studied as an opportunity rather than just an annoying nuisance. Cross-technology communication (CTC) is the key to solve performance issues in co-existence scenarios through collaboration and coordination among co-located networks. After some first approaches forming narrowband IoT waveforms using WiFi chip, meanwhile the spectrum of demonstrated cross-technology communication is huge. For example, commercial WiFi chips can be used to emulate ZigBee, Bluetooth, LTE, LoRa, and more. Such CTC obviously also helps enhancing the resilience of larger scale communication platforms. Offloading, for example, LTE or LoRa traffic on WiFi or vice versa, helps to strengthen the reliability of the communication system as a whole. In this talk, we explore the basic concepts of cross-technology communication and study challenges and opportunities of CTC.