Seminars: Fall 2014


Date      Speaker From Title
  Shlomo Shamai Israel Institute of Technology Information Theory: Old and New-A Personal View
Aug 13 Mischa Dohler Centre for Telecom Research 50 Billion M2M Devices in 5G?
Sep 11 Michael Rice Brigham Young University Space-Time Coding in Aeronautical Telemetry
Sep 11 Jie Xu University of California, Los Angeles Real-time knowledge discovery and decision making from Big Data
Sep 25 Yezekael Hayel University of Avignon, France Advanced Game Theoretical Models and Applications in Networks
Oct 3 Jun Wang The Chinese University of Hong Kong Neurodynamic Optimization Approaches to Robust Pole Assignment for Synthesizing Feedback Control Systems
Oct 16 Deep Medhi University of Missouri-Kansas City How beneficial is Multipath Routing?
Oct 23 Shlomo Shamai Israel Institute of Technology Information Theory: Old and New--A Personal View
Oct 27 Emrah Akyol University of Illinois at Urbana-Champaign Optimal Zero-Delay Communication and Jamming Strategies over Additive Noise Channels
Oct 28 Zhong Zhang Toyohashi University of Technology New Wavelet Transform Design Methods
Dec 3 Mingyue Ji University of Southern California Turning Memory into Bandwidth via Wireless Caching: Fundamental Limits and Practical Challenges
Dec 4 Francisco Javier González-Castaño University of Vigo Analytical Study and QoS-Aware Resource Auction Scheme for Multihop Cognitive Cellular Networks
Dec 5 Rudresh Ghosh University of Texas at Austin Growth and characterization of two dimensional materials: Graphene, hexagonal Boron Nitride and Molybdenum Sulfide
Dec 10 Yonathan Murin Ben-Gurion University Joint Source-Channel Coding in Multiuser Networks
Dec 18 Alexei Ashikhmin Bell Labs, Alcatel-Lucent Inc. Introduction into Massive MIMO Systems


50 Billion M2M Devices in 5G?

Speaker: Mischa Dohler, Centre for Telecom Research
Time: 11:00 am - 12:00 pm, Aug 13, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

A hyper-connected cyber-physical world, by some touted as the 4th Industrial Revolution with unprecedented economic and social opportunities, will heavily rely on the machine-to-machine (M2M) paradigm. Previous designs, which made us believe that low power radios or provisioning of horizontal platforms are driving factors, failed. This talk thus revisits the lessons we learned and how we apply them to invoke architectural and protocol changes to emerging 5G design efforts so that machine type communications (MTC) become a solid constituent of the future IoT connectivity landscape. The talk is based on first-hand experience gained from ETSI M2M, IETF ROLL and other standardization efforts; as well as the successful creation of the pioneering M2M company Worldsensing.

About the Speaker: Mischa Dohler is full Professor in Wireless Communications at King's College London, Head of the Centre for Telecommunications Research, co-founder and member of the Board of Directors of the smart city pioneer Worldsensing, Fellow and Distinguished Lecturer of the IEEE, and Editor-in-Chief of the Transactions on Emerging Telecommunications Technologies.

Dr. Hang holds 13 patents (Taiwan, US and Japan) and has published over 190 technical papers related to image compression, signal processing, and video codec architecture. He was an associate editor (AE) of the IEEE Transactions on Image Processing (1992-1994, 2008-2012) and the IEEE Transactions on Circuits and Systems for Video Technology (1997-1999). He is a co-editor and contributor of the Handbook of Visual Communications published by Academic Press in 1995. He is currently a Distinguished Lecturer and Board Member of the Asia-Pacific Signal and Information Processing Association (APSIPA) (2012- 2013, 2013-2014). He is a recipient of the IEEE Third Millennium Medal and is a Fellow of IEEE and IET and a member of Sigma Xi.

Space-Time Coding in Aeronautical Telemetry

Speaker: Michael Rice, Brigham Young University
Time: 11:00 am - 12:00 pm, Sep 11, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

In air-to-ground radio links, transmission can sometimes be blocked when the aircraft maneuvers place the fuselage between the airborne transmit antenna and the ground-based receive antenna. This problem is usually overcome by using two transmit antennas on the aircraft (say, on the top and bottom). But this creates a new problem: when the same signal is transmitted from both antennas, the communications link suffers from self-interference caused by destructive interference between the two copies of the transmitted radio signal. This talk describes a solution to this problem based on Alamouti space-time block code to overcome the problem. The organization of the talk is as follows: the basic principles of the space-time block code are presented, experimental hardware realizations of the transmitter and receiver are described, and the results of flight tests at Edwards AFB, California are summarized.

About the Speaker: Michael Rice received his PhD from Georgia Tech in 1991. Dr. Rice was with Digital Transmission Systems, Inc. in Atlanta and joined the faculty at Brigham Young University in 1991 where he is currently the Jim Abrams Professor in the Department of Electrical & Computer Engineering. Professor Rice was a NASA/ASEE Summer Faculty Fellow at the Jet Propulsion Laboratory during 1994 and 1995 where he worked on land mobile satellite systems. During the 1999-2000 academic year, Professor Rice was a visiting scholar at the Communication Systems and Signal Processing Institute at San Diego State University.

He was the chair of the Communication Theory Technical Committee in the IEEE Communications Society from 2009 -- 2010. He is currently serving and as Technical Editor for Command, Control and Communication Systems for IEEE Transactions on Aerospace and Electronic Systems. In addition, he is an associate member of the Range Commanders Council, and is the chair of the Signal Processing and Communications Society Chapter in the Utah Section of IEEE.

Real-time knowledge discovery and decision making from Big Data

Speaker: Jie Xu, University of California, Los Angeles
Time: 1:30 pm - 2:30 pm, Sep 11, 2014
Location: 2MTC, 9th floor, Room 9.101, Brooklyn, NY

As the world becomes ever more connected and instrumented, decision-makers have ever more rapid access to ever changing and growing streams of data – but this makes the decision-maker’s problems ever more complex as well, because it is impossible to learn everything in the time frame in which decisions must be made. What the decision-maker must do, therefore, is to discover in real time what is relevant in the enormous stream of data and use the relevant information to make good decisions. This talk presents a systematic framework and associated algorithms that enable a decision-maker to do this, and shows how to use them in real-time traffic prediction as an application scenario. With the vast availability of traffic sensors from which traffic information can be derived in real-time, a lot of research effort has been devoted to developing traffic prediction techniques, which in turn improve route navigation, traffic regulation, urban area planning and etc. One key challenge in traffic prediction is how much to rely on prediction models that are constructed using historical data in real-time traffic situations. Our decision framework learns from the current traffic situation in real-time and predicts the future traffic by matching the current situation to the most effective prediction model. When the traffic prediction involves multiple distributed learners but only the feedback about the overall effect of their decisions is available, we also propose fast learning algorithms by exploiting the informativeness of the global feedback. The algorithms we propose yield strong performance guarantees for both the long run and the short run. The applications are numerous besides traffic prediction, including patient monitoring, surveillance, social networks etc.

About the Speaker: Jie Xu is a final year PhD student at University of California at Los Angeles. He is advised by Prof. Mihaela van der Schaar in the Department of Electrical Engineering. Prior to attending UCLA, he received his BS and MS degrees in Electrical Engineering from Tsinghua University in China and graduated with honor. His research spans the area of machine learning, data mining and game theory, with an emphasis on learning and incentive design in networks.

Advanced Game Theoretical Models and Applications in Networks

Speaker: Yezekael Hayel, University of Avignon
Time: 11:00 am - 12:00 pm, Sep 25, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

Abstract: New communication networks are more and more complex (heterogeneous, dynamic, etc) and mainly more decentralized. Performances of those networks are difficult to determine, even more when the network is composed of non-cooperative agents. Examples of such networks are: new wireless networks like « self-organized networks », social networks, transportation networks, etc. In the last ten years, Game Theory has become one of the more suitable mathematical tools to study and to optimize those decentralized complex networks. In this presentation, I will expose some works related to advanced game theoretical models and applications to decentralized resource allocation problems in networks. In fact, some particular networks assume strong properties about the interactions between the agents and on the network characteristics. Hierarchical games assume a multi-level structure between the decision makers. Important results can be obtained using this type of non-cooperative game for new wireless networks like Cognitive Radio Networks, based on opportunistic spectrum usage. Evolutionary games, inspired by Darwin's evolution principle, are able to propose models for complex networks with large number of players. Particularly, those models take into consideration robustness of the equilibrium against deviations of a fraction of individuals and also can serve as basement to build learning procedures that converge to equilibrium. Finally, routing games, initiated in transportation networks, propose facilities to model management problems in networks dealing with content distribution and to understand how pricing mechanisms can be used to control/to influence usage in different types of networks (social networks, transportation, content, etc).

About the Speaker: Yezekael Hayel is an Assistant Professor with tenure position at University of Avignon in France. He received one M.Sc. in Computer Science and one in Applied Mathematics from the University of Rennes 1, in 2001 and 2002 respectively. He had a Ph.D. in Computer Science from University of Rennes 1 and INRIA in 2005 on Network pricing. His research interests include performance evaluation of networks based on game theoretic and queueing models. He looks at applications in communication and transportation networks like: wireless flexible networks, bio-inspired and self-organizing networks, economics models of the internet and yield management. Recently, his application domains enlarge to transportation networks, social networks and complex networks. Since he has joined the networking group of the LIA/CERI, Yezekael Hayel participates in several national (ANR) and international projects (European, cefipra, etc) with industrial companies like Orange Labs, Alcatel-Lucent, IBM and academic partners like Supelec, CNRS, UCLA. For the academic year 2014-2015, he is a visiting professor at NYU School of Enginerring.

Neurodynamic Optimization Approaches to Robust Pole Assignment for Synthesizing Feedback Control Systems

Speaker: Jun Wang, The Chinese University of Hong Kong
Time: 10:30 am - 11:30 pm, Oct 3, 2014
Location: LC433, 5 MetroTech Center, Brooklyn, NY

Pole assignment (placement) is a basic approach for linear control system design. It is concerned with the assignment of the poles (eigenvalues) and their associated eigenvectors via feedback control laws, which can meet the various closed-loop design specifications in control systems. Given a linear system and the desired closed-loop spectrum, the robust pole assignment problem is to find the feedback gains such that the robustness of the eigensystem is optimized. The robust pole assignment problem was first formulated by means of minimizing the spectral condition number of the eigenvector matrix, as the closed-loop poles move at a rate no greater than the condition number per unit change in the norm of the variation of the closed-loop system matrix. As the spectral condition number is nonconvex, it is difficult to reach its global minima. Although several alternative robustness measures were developed, they are still nonconvex with limited successes by means of conventional or gradient-flow approaches. In this talk, novel neurodynamic optimization approaches to robust pole assignment will be presented for synthesizing linear control systems via state and output feedback. The problem is formulated as a pseudoconvex optimization problem with the spectral condition number as the objective function (robustness measure) and linear matrix equality constraints for exact pole assignment. Two coupled recurrent neural networks are applied for solving the formulated problem in real time. In contrast to existing approaches, the exponential convergence of proposed neurodynamcs to global optimal solutions can be guaranteed even with lower model complexity in terms of the number of variables. Simulation results of the proposed neurodynamic approaches for eleven benchmark problems will be reported to demonstrate their superiority. In addition, the application of the proposed approach to piecewise linear systems will be delineated. The extensions of the present results based on convex reformulations will be also discussed.

About the Speaker: Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published 160 journal papers, 13 book chapters, 10 edited books, and numerous conference papers in these areas. He has been an Associate Editor of the IEEE Transactions on Cybernetics and its predecessor for ten years and a member of the editorial board or editorial advisory board of Neural Networks and International Journal of Neural Systems. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009) and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence. He was an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012) and served in many standing/technical committees such as the President of Asia Pacific Neural Network Assembly (APNNA), IEEE Fellow Committee and IEEE Computational Intelligence Society Fellow and Awards Committees, and currently is on the Board of Governors of the IEEE Systems, Man and Cybernetics Society. He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award, APNNA Outstanding Achievement Award in 2011, and IEEE Neural Networks Pioneer Award in 2014, among other distinctions.

How beneficial is Multipath Routing?

Speaker: Deep Medhi, University of Missouri-Kansas City
Time: 11:00 am - 12:00 pm, Oct 16, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

It's often believed that multipath routing is always beneficial. Taking a traffic engineering perspective, we consider commonly used goals such as congestion minimization, minimum average delay, and minimum cost routing. Using a well-known result from linear programming, we show that the number of paths taken by all demands at optimality is limited by the total number of demands and links in a network. When all node pairs (demands) in a network have traffic, multipath routing essentially becomes single-path routing, especially as the network becomes large when the number of links are in the order of nodes. Under certain traffic and capacity conditions, single-path routing is found to be optimal. We will also present results on a number of traffic scenarios and load conditions using topologies used by ISPs and in data center networks. These observations are counter-intuitive due to our commonly held belief about multipath routing.

About the Speaker: Deep Medhi is Curators' Professor in the Department of Computer Science and Electrical Engineering at the University of Missouri- Kansas City, USA. He received B.Sc. in Mathematics from Cotton College, Gauhati University, India, M.Sc. in Mathematics from the University of Delhi, India, and his Ph.D. in Computer Sciences from the University of Wisconsin-Madison, USA. Prior to joining UMKC in 1989, he was a member of the technical staff at AT&T Bell Laboratories. He was an invited visiting professor at the Technical University of Denmark, a visiting research fellow at Lund Institute of Technology, Sweden, a research visitor at University of Campinas, Brazil under the Brazilian Science Mobility Program and served as a Fulbright Senior Specialist. He is the Editor-in-Chief of Springer’s Journal of Network and Systems Management, and is on the editorial board of IEEE/ACM Transactions on Networking, IEEE Transactions on Network and Service Management, and IEEE Communications Surveys & Tutorials. He is co-author of the books, Routing, Flow, and Capacity Design in Communication and Computer Networks (2004) and Network Routing: Algorithms, Protocols, and Architectures (2007), both published by Morgan Kauffman/Elsevier.

Information Theory: Old and New--A Personal View

Speaker: Shlomo Shamai, Israel Institute of Technology
Time: 11:00 am - 12:00 pm, Oct 23, 2014
Location: Pfizer Auditorium, Bern Dibner Library of Science & Technology, 5 MetroTech Center, Brooklyn, NY

Part of Jack Keil Wolf Lecture Series

The presentation starts by demonstrating in a descriptive way the origin of information theory in Shannon’s 1948 monumental work, and pointing some interdisciplinary aspects within general areas of electrical engineering and beyond. We discuss a change of paradigms in information theory from being a pure mathematical theory of communications to a theory with widescope direct practical implications and applications. To demonstrate the rich aspects of the problems considered and their implications as well as some inter disciplinary connections, we focus on a simple matrix based linear additive Gaussian model. We elaborate on the information-estimation intimate connection, mentioning its impact on non-linear filtering and on recent views of efficient coding in single and multi-terminal channels. Possible extensions to general channels and a short outlook conclude the presentation.

About the Speaker: Shlomo Shamai received the B.Sc., M.Sc., and Ph.D. degrees in electrical engineering from the Technion—Israel Institute of Technology, in 1975, 1981 and 1986 respectively. Since 1986 he is with the Department of Electrical Engineering, Technion-Israel Institute of Technology, where he is now a Technion Distinguished Professor, and holds the William Fondiller Professor of Telecommunications chair. His research interests encompass a wide spectrum of topics in information theory and statistical communications.

Dr. Shamai is an IEEE Fellow and a Member of the Israeli Academy of Sciences and Humanities and a Foreign Member of the US National Academy of Engineering. He is the recepient of the 2014 Rothschild Prize in Mathematics/Computer Sciences and Engineering and the 2011 Claude E. Shannon Award. He has been awarded the 1999 van der Pol Gold Medal of the Union Radio Scientifique Internationale (URSI), and is a co-recipient of the 2000 IEEE Donald G. Fink Prize Paper Award, the 2003, and the 2004 joint IT/COM societies paper award, the 2007 IEEE Information Theory Society Paper Award, the 2009 European Commission FP7, Network of Excellence in Wireless COMmunications (NEWCOM++) Best Paper Award, and the 2014 EURASIP Best Paper Award for the EURASIP Journal on Wireless Communications and Networking. He is the recipient of the 2010 Thomson Reuters Award for International Excellence in Scientific Research and is listed in the 2014 Thomson Reuters "The World's Most Influential Scientific Minds". He is also the recipient of the 2000 Technion Henry Taub Prize for Excellence in Research. He has served as Associate Editor for the Shannon Theory of the IEEE Transactions on Information Theory, and has also served on the Board of Governors of the Information Theory Society. He has served on the Executive Editorial Board of the IEEE Transactions on Information Theory'.

Optimal Zero-Delay Communication and Jamming Strategies over Additive Noise Channels

Speaker: Emrah Akyol, University of Illinois at Urbana-Champaign
Time: 11:00 am - 12:00 pm, Oct 27, 2014
Location: LC433, 5 MetroTech Center, Brooklyn, NY

This talk is concerned with optimal zero-delay communication and jamming strategies over additive noise channels. First, we present the recently discovered characterization of conditions for linearity of optimal estimation, and its extension to communication settings in the context of zero-delay source-channel coding. Next, we consider the problem of optimal zero-delay jamming over an additive noise channel. Early work had solved this problem for a scalar Gaussian source and a scalar Gaussian channel. Building on a sequence of recent results on conditions for linearity of optimal estimation, and of optimal mappings in source-channel coding, the saddle-point solution to the jamming problem is derived for general sources and channels, without recourse to Gaussianity assumptions. The linearity conditions are shown to play a pivotal role in jamming, in the sense that the optimal jamming strategy is to effectively force both the transmitter and the receiver to default to linear mappings, i.e., the jammer ensures, whenever possible, that the transmitter and the receiver cannot benefit from non-linear strategies. The conditions and general settings where such unbeatable strategy can indeed be achieved by the jammer are analyzed. The analysis is also extended to vector spaces which involves a new aspect of optimization: the allocation of available transmit and jamming power over source and channel components. The optimal power allocation strategies for the jammer and the transmitter have an intuitive interpretation as the jammer allocates power according to water-filling over the channel eigenvalues, while the transmitter performs water-pouring (reverse water-filling) over the source eigenvalues. We finally present our numerical approaches-based on deterministic annealing- to such zero-delay communication and jamming problems which, as a byproduct, provide the best numerical solutions to the benchmark problems in decentralized control such as the well known Witsenhausen’s counterexample.

About the Speaker: Emrah Akyol received the Ph.D. degree in 2011 from the University of California at Santa Barbara. From 2006 to 2007, he held positions at Hewlett-Packard Laboratories and NTT Docomo Laboratories, both in Palo Alto, where he worked on topics in video compression. From 2013 to 2014, Dr. Akyol was a postdoctoral researcher in the Electrical Engineering Department, University of Southern California.

Currently, Dr. Akyol is a postdoctoral research associate in the Coordinated Science Laboratory, University of Illinois at Urbana-Champaign. His current research is on the interplay of networked information theory, game theory, communications, sensing and control. Dr. Akyol received the 2010 UCSB Dissertation Fellowship and the 2014 USC Postdoctoral Training Award.

New Wavelet Transform Design Methods

Speaker: Zhong Zhang, Toyohashi University of Technology
Time: 11:00 am - 12:00 pm, Oct 28, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

The discrete wavelet transform (DWT) has been applied widely as a tool for signal and image processing, due to several good properties: 1) Perfect reconstruction and fast calculation using the multiresolution algorithm (MRA); 2) High compressibility using an orthogonal (or bi-orthogonal) mother wavelet with octave band-pass filter characteristics. However, the fast MRA algorithm uses down-sampling, and therefore the DWT lacks shift invariance. In addition, octave analysis is not necessarily the most suitable approach for the signal under analysis. Many techniques have been suggested to improve the DWT shift invariance -- in particular, the Dual-tree Complex Discrete Wavelet Transform, proposed by Kingsbury. But the design of the complex wavelet is a problem. In order to improve the octave analysis, a best-basis method has been proposed in which each filter band is first subdivided using a wavelet packet, and the most suitable basis is then selected by evaluating the calculation cost of the wavelet coefficients. However, the selectable frequency band is limited, and the chosen basis is not necessarily the most suitable. To solve these problems, in this talk, we will introduce a method to design freely new wavelets and achieve new wavelet transforms for different applications.

About the Speaker: Zhong ZHANG is a professor of the Mechanical Engineering department at Toyohashi University of Technology. He received his Bachelor engineering, Master engineering degrees in 1982 and 1984, respectively, from Chang'an Highway University, China and his Doctor engineering degree in 1993 from Okayama University, Japan. He was a visiting scholar at the University of Melbourne, Australia in 1998. He was engaged in research regarding intelligent systems and signals, and image processing as a senior researcher at the Industrial Technology Center of Okayama Prefecture, an associate professor at Okayama Prefecture University, and now a professor at instrumentation systems Laboratory of Toyohashi University of Technology.

Turning Memory into Bandwidth via Wireless Caching: Fundamental Limits and Practical Challenges

Speaker: Mingyue Ji, University of Southern California
Time: 2:00 pm - 3:00 pm, Dec 3, 2014
Location: 2MTC, 9th floor, Room 9.101, Brooklyn, NY

Video is responsible for 66% of the 100x increase of wireless data traffic predicted in the next few years. Traditional methods for network capacity increase are very costly, and do not exploit the unique features of video. This talk gives a survey of a novel transmission paradigm based on the following two key properties: (i) video shows a high degree of asynchronous content reuse, and (ii) storage is the fastest-increasing quantity in modern hardware. Based on these properties, we suggest caching at wireless edge, namely, caching in helper stations (femto-caching) and/or directly into the user devices. We study two fundamentally different network structures: device-to-device (D2D) caching networks and shared link caching networks.

First, we present results based on uncoded D2D transmissions and network coded multicast delivery that show a “Moore’s law” for throughput: namely, in a certain regime of sufficiently high content reuse and/or sufficiently high aggregate storage capacity (sum of the storage capacity of all the users) in the network, the per-user throughput increases linearly, or even super-linearly with the cache size, and it is independent of the number of users for large network size, despite the fact that these users make independent and individual video files requests, i.e., the system does not exploit the naive broadcasting property of the wireless medium to send the same source to everybody. On the other hand, for both considered networks, we also provide information theoretic converse, by using which, we show that the proposed schemes achieves the order-optimal capacity. Then, we present the practical challenges and limitations of the achievable schemes. In particular, we focus on the shared link networks. To overcome these challenges, we design a novel polynomial-time complexity coding algorithm, which achieves near optimal performance such that it preserves the promised “Moore’s law” for throughput under realistic network parameter regimes.

About the Speaker: Mingyue Ji is a final year PhD candidate at Ming Hsieh Department of Electrical Engineering, University of Southern California (USC). His adviser is Professor Giuseppe Caire. Prior to USC, he worked as a research engineer and finished his Master thesis at the Access Technologies and Signal Processing Group in Ericsson, Stockholm, Sweden. He also obtained his Master of Science (MS) Degree in Electrical Engineering at Royal Institute of Technology (KTH), Sweden, and obtained his Bachelor Degree in Communication Engineering at Beijing University of Posts and Telecommunications (BUPT), China.

Mingyue Ji is interested the broad area of information theory, coding theory, concentration of measure and statistics with the applications of caching networks, wireless communication, distributed storage, and (statistical) signal processing. He is a co-recipient of the best student paper award in IEEE European Wireless 2010 Conference and received USC Annenberg Graduate Fellowship from 2010 to 2014.

Analytical Study and QoS-Aware Resource Auction Scheme for Multihop Cognitive Cellular Networks

Speaker: Francisco Javier González-Castaño, University of Vigo
Time: 11:00 am - 12:00 pm, Dec 4, 2014
Location: Brooklyn, NY

We extend the model in B. Lorenzo, S. Glisic, “Context Aware Nano Scale Modeling of Multicast Multihop Cellular Network”, IEEE/ACM Transactions on Networking, vol. 21, no. 2, pp. 359 – 372, April 2013, which proposes a multihop model for cellular networks, to consider cognitive resource allocation. Performance is characterized by means of Markov analysis.

On top of the model, we propose a QoS-aware resource action scheme. The results for theoretical networks suggest that the model & its resource allocation optimization are realistic for current microwave technologies.

About the Speaker: Francisco Javier González-Castaño is a Full Professor with the Department of Telematics Engineering, University of Vigo, Spain. He is also with Gradiant, Spain, as Scientific Advisor. He leads the Information Technologies Group, University of Vigo, Spain. He has published over seventy papers in international journals, in the fields of telecommunications and computer science, and he has participated in several relevant national and international projects. He holds two US patents.

Growth and characterization of two dimensional materials: Graphene, hexagonal Boron Nitride and Molybdenum Sulfide

Speaker: Rudresh Ghosh, University of Texas at Austin
Time: 11:00 am - 12:00 pm, Dec 5, 2014
Location: LC433, 5 MetroTech Center, Brooklyn, NY

Over the last decade, since the demonstration of exceptional physical, chemical and electrical properties of graphene, there has been a lot of interest in two-dimensional materials. Of these new materials significant effort has been focused on transition metal dichalcogenides (TMDs) due to their various possible applications. Initial work on TMDs, similar to that of graphene, has depended on exfoliated samples. In this work we present controlled large-area synthesis of highly crystalline few to monolayers of various TMDs (MoS2, WS2, WSe2) using both solid and gas precursors. Characterization of the TMDs are done using a combination of conventional techniques such as Raman and Photoluminescence spectroscopy, Atomic force microscopy, scanning and transmission electron microscopy. New characterization tools with the capability of localized dielectric mapping (Microwave impedance microscopy) and elemental identification of individual layers and their interfaces (using Time of Flight SIMS) are demonstrated as extremely useful for studying these 2d materials. Electrical device characterization and paths of optimization are also presented.

About the Speaker: Dr. Rudresh Ghosh received his Bachelors of Science degree from the University of Calcutta (2004) and his Masters of Science from the Indian Institute of Technology, Bombay (2006), both in Physics. For his PhD, he worked with Dr. Rene Lopez at the University of North Carolina at Chapel Hill. As part of the Energy Frontier Research Center his work focused on the growth of thin films of metal oxides and their application for photovoltaic applications. After obtaining his PhD (2012) he moved to the University of Texas at Austin where he currently works as a post-doctoral fellow at the Microelectronics Research Center. His current research focusses on the growth and characterization of 2-dimensional materials beyond graphene.

Joint Source-Channel Coding in Multiuser Networks

Speaker: Yonathan Murin, Ben-Gurion University
Time: 1:00 pm - 2:00 pm, Dec 10, 2014
Location: 2MTC, 9th floor, Room 9.101, Brooklyn, NY

The simplest approach to the transmission of a source signal over a noisy channel is to first encode (compress) the source via source codes and obtain a bit sequence for each source, and then, encode these bits via a channel code by adding redundancy. This technique, commonly known as separate source-channel coding, is prevalent in modern communication systems. However; it is well known that in many scenarios source-channel separation is sub- optimal and higher source-channel rates can be achieved by designing the channel and the source code jointly, known as joint source-channel coding (JSCC). In this talk we discuss JSCC for two multi-user scenarios.

The first scenario involves cooperative transmission of two correlated sources over a multiple-access relay channel, which represents cooperative uplink communication in wireless networks. We shall present novel cooperative joint-source channel coding schemes using combinations of Slepian-Wolf coding and derive new necessary conditions.

The second scenario is the transmission of correlated Gaussian sources over a Gaussian broadcast channel with noiseless feedback. This scenario is motivated by practical network control problems with strict delay constraints, e.g., a sensor node communicating the state of a complex system to various control units. Differently from the existing literature, we focus on the finite horizon regime. We present three new joint source-channel transmission schemes and characterize their performances. The first scheme is based on communication theoretic arguments, the second one builds upon liner quadratic Gaussian control theory, and the third applies a dynamic programming approach.

This is a joint work with Ron Dabora (BGU), Deniz Gunduz (ICL), and Yonatan Kaspi (UCSD).

About the Speaker: Yonathan Murin received his B.Sc. degree in Electrical Engineering and Computer Science from Tel-Aviv University, Israel, in 2004, and his M.Sc. (magna cum laude) degree in Electrical Engineering from Ben-Gurion University, Israel, in 2011. He is currently pursuing a Ph.D. degree in Electrical Engineering at Ben-Gurion University. From 2004 to 2010 he worked as a DSP and algorithms engineer and as a team leader at Comsys Communication and Signal Processing. His research interests include network information theory, wireless communications and digital signal processing. Yonathan is a recipient of the Zin and Pratt fellowships for outstanding Ph.D. students.

Introduction into Massive MIMO Systems

Speaker: Alexei Ashikhmin, Bell Labs, Alcatel-Lucent Inc.
Time: 11:00 am - 12:00 pm, Dec 18, 2014
Location: 2MTC, 10th floor, Room 10.099, Brooklyn, NY

Massive MIMO techniques can tremendously improve the performance of modern wireless networks. Massive MIMO base stations are equipped with a very large number of antennas (possibly tens to hundreds) and simultaneously serve multiple users on the same frequency band. This approach allows obtaining large gains in spectral-efficiency and energy efficiency compared with conventional MIMO technology. As the number of antennas grows large, the effects of noise and fast fading vanish and intra-cell and inter-cell interference can be mitigated using simple linear precoding and detection methods. Large-scale MIMO, therefore, has the potential to have a significant impact on the future wireless communications systems.

In this introductory talk I will outline the main technical aspects of massive MIMO systems including TDD protocol, channel estimation, detection, and precoding. I will also discuss challenges and opportunities associated with implementing massive MIMO in the future wireless communications systems.

About the Speaker: Alexei Ashikhmin is a research scientist in the Communications and Statistical Sciences Department of Bell Labs, Murray Hill, New Jersey. Alexei Ashikhmin's research interests include communications theory, large Scale antenna arrays, classical and quantum information theory, and the theory of error correcting codes. From 2003 to 2006, and from 2011 to 2014 Dr. Ashikhmin served as an Associate Editor for IEEE Transactions on Information Theory.

In 2004 Dr. Ashikhmin received S.O.Rice Award for the 2004 best paper of IEEE Transactions on Communications. In 2002, 2010, and 2011 Dr. Ashikhmin received Bell Laboratories President Awards for breakthrough research in wired and wireless communication projects.

In 2005-2007 Alexei Ashikhmin was an adjunct professor at Columbia University. He taught courses "Error Correcting Codes" and "Communications Theory".