Title: How to achieve massive MIMO gains in FDD systems
Massive MIMO is a powerful multiuser/multiantenna technology that exploits a very large number of antennas at the base station side and the knowledge of the channel matrix between base station antennas and multiple users in order to achieve large beamforming and multiplexing gain. Classical massive MIMO exploits Time-Division Duplexing (TDD) and channel reciprocity, such that the channel matrix can be learned at the base station from the incoming uplink pilot signals sent by the users. However, the large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where channel reciprocity does not hold and explicit downlink probing and uplink CSI feedback are required in order to achieve some spatial multiplexing gain. Unfortunately, the overhead incurred by explicit probing and feedback is very large in massive MIMO, since the channels are high-dimensional random vectors. In this paper, we present a new approach to achieve very competitive tradeoff between spatial multiplexing gain and probing/feedback overhead in FDD massive MIMO. Our approach is based on two novel concepts: 1) an efficient and mathematically rigorous technique to extrapolate the channel covariance matrix from the uplink to the downlink, such that the second order statistics of each downlink channel can be accurately learned for free from uplink pilots; 2) a novel ''sparsifying precoding'' approach, that introduces sparsity in the channel in a controlled form, such that for any assigned overhead (i.e., downlink pilot dimension) it is possible to set an optimal sparsity level for which the ''effective'' channels after sparsification can be estimated at the base station with low mean-square error. We compare our method with that of the state-of-the-art compressed sensing (CS) based method. Our results show that the proposed method is much more robust than compressed sensing methods, since it is able to ''shape the channel sparsity'' as desired, instead of being at the mercy of nature (i.e., at the mercy of the natural sparsity induced by the propagation environment).
Giuseppe Caire was born in Torino, Italy, in 1965. He received the B.Sc. in Electrical Engineering from Politecnico di Torino in 1990, the M.Sc. in Electrical Engineering from Princeton University in 1992 and the Ph.D. from Politecnico di Torino in 1994. He is currently an Alexander von Humboldt Professor with the Electrical Engineering and Computer Science Department of the Technical University of Berlin, Germany. He has served as Associate Editor for the IEEE Transactions on Communications and as Associate Editor for the IEEE Transactions on Information Theory. He received the Jack Neubauer Best System Paper Award from the IEEE Vehicular Technology Society in 2003, the IEEE Communications Society & Information Theory Society Joint Paper Award in 2004 and in 2011, the Okawa Research Award in 2006, the Alexander von Humboldt Professorship in 2014, and the Vodafone Innovation Prize in 2015. Giuseppe Caire is a Fellow of IEEE since 2005. He has served in the Board of Governors of the IEEE Information Theory Society from 2004 to 2007, and as officer from 2008 to 2013. He was President of the IEEE Information Theory Society in 2011. His main research interests are in the field of communications theory, information theory, channel and source coding, with particular focus on wireless communications.
Title: Understanding channel dynamics in millimeter wave cellular
A critical challenge for wireless communications in the millimeter wave (mmWave) bands is blockage. MmWave signals suffer significant penetration losses from many common materials and objects, and small changes in the position of obstacles in the environment can cause large variations in the channel quality. This paper provides a measurement-based study of the effects of human blockage on an end-to-end application over a mmWave cellular link. A phased array system is used to measure the channel in multiple directions almost simultaneously in a realistic indoor scenario. The measurements are integrated into a detailed ns-3 simulation that models both the latest 3GPP New Radio beam search procedure as well as the internet protocol stack. The measurement-based simulation illustrates how recovery from blockage depends on the path diversity and beam search.
Sundeep Rangan received the B.A.Sc. from the University of Waterloo, Canada and the M.Sc. and Ph.D. degrees from the University of California, Berkeley, all in Electrical Engineering. In 2000, he co-founded (with four others) Flarion Technologies, a spin-off of Bell Labs that developed Flash OFDM, one of the first cellular OFDM data systems and pre-cursor to 4G systems including LTE and WiMAX. In 2006, Flarion was acquired by Qualcomm Technologies where Dr. Rangan was a Director of Engineering involved in OFDM infrastructure products. He joined the ECE department at NYU Tandon (formerly NYU Polytechnic) in 2010. He is a Fellow of the IEEE and Director of NYU WIRELESS, an academic-industry research center researching next-generation wireless systems. His research interests are in wireless communications, signal processing, information theory and control theory.
Title: Network inference and its application to the estimation of crowd dynamics from IoT sensors
In this paper, we explore the application of system identification techniques to the inference of the network dynamical model that characterizes crowd dynamics. We focus then on sensor observations of pedestrians' actions considering that wearables, smart mobile phones and other IoT devices embedded in the environment give significant insights on their expected mobility patterns. Most models for tracking mobility ignore the strong coupling between the model-agents as well as their surroundings while we capture in our problem the swarming behavior of the network, including both their social interactions and their interest in different sites in the environment. The model that captures the pedestrian dynamics is loosely based on the social force model proposed by Helbing and Molnar. This is used as a parametric system model that informs our network inference formulation.
Anna Scaglione is currently a professor in electrical and computer engineering at Arizona State University. Dr. Scaglione's expertise is in the broad area of statistical signal processing for communications, electric power systems and networks. Her current research focuses on studying and enabling decentralized learning and signal processing in networks of sensors. Dr. Scaglione is a fellow of IEEE. She served in the IEEE in many capacities, including as Associate Editor for the IEEE Transactions on Wireless Communications and on Signal Processing and Editor in Chief of the IEEE Signal Processing Letters. She was member of the Signal Processing Society Board of Governors from 2011 to 2014. She received the 2000 IEEE Signal Processing Transactions Best Paper Award and more recently she was honored for the 2013 IEEE Donald G. Fink Prize Paper Award for the best review paper in that year in the IEEE publications. Her work with her student (Lin Li) earned the 2013 IEEE Signal Processing Society Young Author Best Paper Award.
Title: Analysis of some well-rounded lattices in wiretap channels
Recently, various criteria for constructing wiretap lattice coset codes have been proposed, most prominently the minimization of the so-called flatness factor. However, these criteria are not constructive per se. As explicit constructions, well-rounded lattices have been proposed as possible minimizers of the flatness factor, but no rigorous proof has been given. In this paper, we study various well-rounded lattices, including the best sphere packings, and analyze their shortest vector lengths, minimum product distances, and flatness factors, with the goal of acquiring a better understanding of the role of these invariants regarding secure communications. Simulations are carried out in dimensions four and eight, yielding the conclusion that the best sphere packing does not necessarily yield the best performance, not even when compared to other well-rounded lattices having the same superlattice. This motivates further study and construction of well-rounded lattices for physical layer security.
Camilla Hollanti received the M.Sc. and Ph.D. degrees from the University of Turku, Finland, in 2003 and 2009, respectively, both in pure mathematics. Since 2011, she has been with the Department of Mathematics and Systems Analysis at Aalto University, Finland, where she currently works as Associate Professor and leads a research group in Algebra, Number Theory, and Applications. She is also affiliated with the Institute of Advanced Studies at the Technical University of Munich, where she holds a 3-year Hans Fischer Fellowship. Dr. Hollanti is an editor of the AIMS Journal on Advances in Mathematics of Communications. She is a recipient of several grants, including five Academy of Finland grants in 2010-2016. In 2014, she received the World Cultural Council Special Recognition Award for young researchers, and in 2017 the Finnish Academy of Science and Letters awarded her the Väisälä Prize in Mathematics. Her research interests lie within applications of algebraic number theory to wireless communications and physical layer security, as well as in combinatorial and coding theoretic methods related to distributed storage systems.
Title: Foundation of physical layer security for message transmission and storage
In this survey we contrast the classical transmission theory of Shannon and the identification theory of Ahlswede and Dueck. We show that it may be useful to take a closer look at the recipient's goals. In identification theory, the recipient's goal is changed, thereby significantly increasing performance. Furthermore, there are further gains in the secure transmission of data. In this work we show the basic idea of the identification theory on the discrete memoryless channel. We review the results for robust and secure channels and jamming attacks. Furthermore, we give examples of applications. Very important for the practical application are also the analysis of the capacity function. It turns out that there may be places of discontinuity.
Holger Boche received the Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering from the Technische Universität Dresden, Dresden, Germany, in 1990 and 1994, respectively. He graduated in mathematics from the Technische Universität Dresden in 1992. He received his Dr. rer. nat. degree in pure mathematics from the Technische Universität Berlin, Berlin, Germany, in 1998. Since October 2010 he has been with the Institute of Theoretical Information Technology and Full Professor at the Technische Universität München (TUM), Munich, Germany. Since 2014 he has been a member and honorary fellow of the TUM Institute for Advanced Study, Munich, Germany. Prof. Boche is a Member of the IEEE Signal Processing Society SPCOM and SPTM Technical Committees and a fellow of the IEEE. He was elected a Member of the German Academy of Sciences (Leopoldina) in 2008 and of the Berlin Brandenburg Academy of Sciences and Humanities in 2009. He received the Research Award "Technische Kommunikation" from the Alcatel SEL Foundation in October 2003, the "Innovation Award" from the Vodafone Foundation in June 2006, and the Gottfried Wilhelm Leibniz Prize from the Deutsche Forschungsgemeinschaft (German Research Foundation) in 2008. He was co-recipient of the 2006 IEEE Signal Processing Society Best Paper Award and recipient of the 2007 IEEE Signal Processing Society Best Paper Award. He was the General Chair of the Symposium on Information Theoretic Approaches to Security and Privacy at IEEE GlobalSIP 2016. Among his publications is the recent book Information Theoretic Security and Privacy of Information Systems (Cambridge University Press).
Title: Adversarial machine learning: The case of optimal attack strategies against recommendation systems
Learning with expert advice framework has drawn much attention in recent years especially in the context of recommendation systems. We consider two challenges that we face in broadly applying this framework in practice. One is the impact of adversarial attack strategies (malicious recommendations) and the other is lack of sufficient recommendation from quality experts (aka sleeping expert setting). In this paper, we discuss some recent results on understanding adversarial strategies and their effect on recommendation systems. In addition, in the sleeping expert setting, we discuss some novel designs for learning algorithms and the analysis of their convergence properties.
Negar Kiyavash is Willett Faculty Scholar at the University of Illinois and a joint Associate Professor of Industrial and Enterprise Engineering and Electrical and Computer Engineering. She is also affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2006. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF CAREER and AFOSR YIP awards and the Illinois College of Engineering Dean's Award for Excellence in Research.
Title: Online learning adaptive to dynamic and adversarial environments
The present contribution deals with online learning of functions, where multi-kernel approaches, among other popular methods, have well-documented merits but also face major challenges. Leveraging the random feature approximation, an online multi-kernel learning scheme is developed to infer the intended nonlinear function. To account for dynamic and possibly adversarial environments, an adaptive and scalable multi-kernel learning scheme is also introduced at affordable complexity and memory requirements. Performance guarantees are provided in terms of dynamic regret analysis, while numerical tests on a Twitter dataset are carried out to showcase the effectiveness of our approach.
Georgios B. Giannakis received his Diploma in Electrical Engineering from the National Technical University of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engineering, 1986. He was with the University of Virginia from 1987 to 1998, and since 1999 he has been a professor with the Univ. of Minnesota, where he holds an Endowed Chair in Wireless Telecommunications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing. His current research focuses on learning from Big Data, wireless cognitive radios, and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the IEEE Fourier Technical Field Award (2015). He is a Fellow of the IEEE and the EURASIP, and has served the IEEE in a number of posts, including that of a Distinguished Lecturer for the IEEE-SP Society.
Title: Multi-layered networks
Many real-world complex systems can be described by a network structure, where a set of elementary units, e.g, human, gene, sensor, or other types of 'nodes' are connected by edges that represent dyadic relations, e.g., an observed interaction or an inferred dependence measured by correlation or mutual information. Such so-called relevance networks can be undirected or directed graphs depending on whether the relevance measure is symmetric or asymmetric. Often there are multiple ways that pairs of nodes might be related, e.g., by family ties, friendships, and professional connections in a social network. A multi-layer relevance network can be used to simultaneously capture these different types of relations. Dynamic relevance networks whose edges change over time are a type of multi-layer network, with each layer representing relations at a particular time instant. In this paper, we review and discuss multi-layer relevance network models in the context of relevance measures and node centrality for datasets with multivalent relations. We illustrate these models for dynamic gene regulatory networks and dynamic social networks.
Alfred O. Hero III is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is also the Co-Director of the University's Michigan Institute for Data Science (MIDAS). His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D from Princeton University (1984), both in Electrical Engineering. He is a Fellow of the IEEE. He has served as President of the IEEE Signal Processing (SP) Society and as a member of the IEEE Board of Directors. He has received numerous awards for his scientific research and service to the profession including several best paper awards, the IEEE SP Society Technical Achievement Award in 2013 and the 2015 Society Award, which is the highest career award bestowed by the IEEE SP Society. He received a Rackham Distinguished Faculty Achievement Award in 2011 and the 2017 Stephen S. Attwood Excellence in Engineering Award, from the University of Michigan. Alfred Hero's recent research interests are in high dimensional spatio-temporal data, multi-modal data integration, statistical signal processing, and machine learning. Of particular interest are applications to social networks, network security and forensics, computer vision, and personalized health.
Title: Multimodal image processing with coupled dictionary learning
In real-world scenarios, many data processing problems often involve heterogeneous images associated with different imaging modalities. Since these multimodal images originate from the same phenomenon, it is realistic to assume that they share common attributes or characteristics. In this paper, we propose a multi-modal image processing framework based on coupled dictionary learning to capture similaries and disparities between different image modalities. In particular, our framework can capture favorable structure similarities across different image modalities such as edges, corners, and other elementary primitives in a learned sparse transform domain, instead of the original pixel domain, that can be used to improve a number of image processing tasks such as denoising, inpainting, or super-resolution. Practical experiments demonstrate that incorporating multimodal information using our framework brings notable benefits.
Miguel Rodrigues is a Reader in Information Theory and Processing with the Department of Electronic and Electrical Engineering, University College London, UK. He obtained his Licenciatura degree in Electrical and Computer Engineering from the University of Porto, Portugal and the Ph.D. in Electronic and Electrical Engineering from University College London, UK. Dr. Rodrigues' most relevant contributions have ranged from the information-theoretic analysis and design of communications systems, information-theoretic security, information-theoretic analysis and design of sensing systems, and, more recently, foundations of machine learning and deep learning problems. His work has also been honored with the IEEE Communications and Information Theory Societies Joint Paper Award 2011. Dr. Rodrigues is currently Co-Chairing the Conference on "Mathematics of Data: Structured Representations for Sensing, Approximation, and Learning" organized under the auspices of the Isaac Newton Institute for Mathematical Sciences programme on "Approximation, Sampling and Compression in Data Science". He serves as Associate Editor for the IEEE Communications Letters and he is co-editing (with Y. C. Eldar) a book on Information-Theoretic Methods in Data Science to be published by Cambridge University Press. Dr. Rodrigues has received the Prize Eng. Antonio de Almeida, Prize Eng. Cristiano Spratley, the Merit Prize from the University of Porto, Portugal, and fellowships from the Portuguese Foundation for Science and Technology as well as the Foundation Calouste Gulbenkian.
Title: Deep tree models for 'big' biological data
The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems across the spectrum of biological studies. Most of the early statistical approaches were ineffective in practice, because the amount of data required for reliable modelling grew exponentially with memory length. On the other hand, many of the more modern methodological approaches that make use of more flexible and parsimonious models result in algorithms that do not scale well and are computationally ineffective for larger data sets. In this paper we describe a class of novel methodological tools for effective Bayesian inference for general discrete time series, motivated primarily by questions regarding data originating from studies in genetics and neuroscience. Our starting point is the development of a rich class of Bayesian hierarchical models for variable-memory Markov chains. The particular prior structure we adopt makes it possible to design effective, linear-time algorithms that can compute most of the important features of the relevant posterior and predictive distributions without resorting to Markov chain Monte Carlo simulation. The origin of some of these algorithms can be traced to the family of Context Tree Weighting (CTW) algorithms developed for data compression since the mid-1990s. We have used the resulting methodological tools in numerous application-specific tasks (including prediction, segmentation, classification, anomaly detection, entropy estimation, and causality testing) on data from different areas of application. The results obtained compare quite favourably with those obtained using earlier approaches, such as Probabilistic Suffix Trees (PST), Variable-Length Markov Chains (VLMC), and the class of Markov Transition Distributions (MTD).
Ioannis Kontoyiannis received the B.Sc. degree in mathematics in 1992 from Imperial College London, UK, and in 1993 he obtained a distinction in Part III of the Cambridge University Pure Mathematics Tripos. In 1997 he received the M.S. degree in statistics and in 1998 the Ph.D. degree in electrical engineering, both from Stanford University. Since 2005 he has been with the Department of Informatics of the Athens University of Economics and Business, where he is currently a Professor. In January 2018 he joined the Information Engineering Division of the University of Cambridge, as Professor of Information and Communications. He has received a number of distinctions including the Manning endowed assistant professorship, a Sloan Foundation Research Fellowship, an honorary Master of Arts Degree Ad Eundem by Brown University, and a two-year Marie Curie Fellowship. He is a Fellow of the IEEE. He has served on the editorial board of the American Mathematical Society's Quarterly of Applied Mathematics journal, the IEEE Transactions on Information Theory, Springer-Verlag's Acta Applicandae Mathematicae, Springer-Verlag's Lecture Notes in Mathematics book series, and the online journal Entropy. He also served a short term as Editor-in-Chief of the IEEE Transactions on Information Theory. His research interests include data compression, applied probability, information theory, statistics, and mathematical biology.
Title: Experimental molecular communication testbed based on magnetic nanoparticles in duct flow
Simple and easy to implement testbeds are needed to further advance molecular communication research. To this end, this paper presents an in-vessel molecular communication testbed using magnetic nanoparticles dispersed in an aqueous suspension as they are also used for drug targeting in biotechnology. The transmitter is realized by an electronic pump for injection via a Y-connector. A second pump provides a background flow for signal propagation. For signal reception, we employ a susceptometer, an electronic device including a coil, where the magnetic particles move through and generate an electrical signal. We present experimental results for the transmission of a binary sequence and the system response following a single injection. For this flow-driven particle transport, we propose a simple parameterized mathematical model for evaluating the system response.
Robert Schober received the Diplom (Univ.) and the Ph.D. degrees in electrical engineering from the Friedrich-Alexander-University of Erlangen-Nuremberg (FAU), Germany, in 1997 and 2000, respectively. Since January 2012 he is an Alexander von Humboldt Professor and the Chair for Digital Communication at FAU. Dr. Schober received several awards for his work including the 2002 Heinz Maier-Leibnitz Award of the German Science Foundation (DFG), the 2004 Innovations Award of the Vodafone Foundation for Research in Mobile Communications, the 2006 UBC Killam Research Prize, the 2007 Wilhelm Friedrich Bessel Research Award of the Alexander von Humboldt Foundation, the 2008 Charles McDowell Award for Excellence in Research from UBC, a 2011 Alexander von Humboldt Professorship, a 2012 NSERC E.W.R. Stacie Fellowship, and a 2017 Wireless Communications Recognition Award. Furthermore, he has been listed as a 2017 Highly Cited Researcher by Clarivate Analytics. He is a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada, and a Fellow of the IEEE. From 2012 to 2015, he served as Editor-in-Chief of the IEEE Transactions on Communications. Currently, he is the Chair of the Steering Committee of the IEEE Transactions on Molecular, Biological and Multiscale Communication and serves on the Editorial Board of the Proceedings of the IEEE. Furthermore, he is a Member at Large of the Board of Governors and a Distinguished Lecturer of the IEEE Communications Society. His research interests fall into the broad areas of Communication Theory, Wireless Communications, and Statistical Signal Processing.
Title: Capacity limits and design principles of molecular communication systems
We consider molecular communication systems and show it is possible to train detectors without any knowledge of the underlying channel models. In particular, we demonstrate that a technique we previously developed, which is called sliding bidirectional recurrent neural network (SBRNN), performs well for a wide range of channel states when it is trained using a dataset that contains many sample transmissions under various channel conditions. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector (VD) with imperfect channel state information (CSI) and it is computationally efficient.
Andrea Goldsmith is the Stephen Harris professor in the School of Engineering and a professor of Electrical Engineering at Stanford University. She co-founded and served as Chief Technical Officer of Plume WiFi and of Quantenna (QTNA) and she currently serves on the Corporate or Technical Advisory Boards of Crown Castle Inc. (CCI), Interdigital Corp. (IDCC), Sequans (SQNS), Quantenna (QTNA) and Cohere. She has also held industry positions at Maxim Technologies, Memorylink Corporation, and AT&T Bell Laboratories. Dr. Goldsmith is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, a Fellow of the IEEE and of Stanford, and has received several awards for her work, including the IEEE ComSoc Edwin H. Armstrong Achievement Award as well as Technical Achievement Awards in Communications Theory and in Wireless Communications, the National Academy of Engineering Gilbreth Lecture Award, the IEEE ComSoc and Information Theory Society Joint Paper Award, the IEEE ComSoc Best Tutorial Paper Award, the Alfred P. Sloan Fellowship, the WICE Technical Achievement Award, and the Silicon Valley/San Jose Business Journal's Women of Influence Award. She is author of the book Wireless Communications and co-author of the books MIMO Wireless Communications and Principles of Cognitive Radio, all published by Cambridge University Press, as well as an inventor on 28 patents. Her research interests are in information theory and communication theory, and their application to wireless communications and related fields. She received the B.S., M.S. and Ph.D. degrees in Electrical Engineering from U.C. Berkeley.