IEEE ISIT2011 TPC Chairs Contact E-Mail: Tutorials
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T-1: Yonina Eldar - Xampling: Analog-to-digital at Sub-Nyquist rates [Abstract] [Slides]
T-2: Abbas El Gamal and Young-Han Kim - Elements of Network Information Theory [Abstract] [Slides]
T-3: Michael Gastpar and Bobak Nazer - Algebraic Structure in Network Information Theory [Abstract] [Slides]
T-4: Michael Tsfasman - Geometry of error correcting codes and sphere packing [Abstract]

T-1: Xampling: Analog-to-digital at Sub-Nyquist rates [Slides]

Presenter: Yonina Eldar

Sunday, July 31, 9:30 - 13:00, [Red 10]


Signal processing methods have changed substantially over the last several decades with an increasing number of operations being shifted from analog to digital. Traditionally, and perhaps the most common practice in engineering, the continuous signal is converted to a discrete stream of numbers by uniform sampling at the Nyquist rate, namely at twice the highest frequency component. While Nyquist-rate sampling is widely popular, it may become a serious bottleneck in modern applications, where signals can span a prohibitively large range of the spectrum.

This tutorial provides an overview of reduced-rate sampling strategies. We refer to this methodology as Xampling: A combination of compression and sampling, performed simultaneously. Xampling merges results from standard sampling theory with recent developments in the field of compressed sensing in order to directly sample a wide class of analog signals at very low rates using existing hardware devices. This paradigm relies on exploiting structure inherent to many different classes of signals, which can be modeled mathematically as a union of subspaces.

We begin with classic methods, such as uniform undersampling, periodic nonuniform sampling and radio-frequency demodulation. We then consider flexible signal models, in which there is an uncertainty about the input location, so that the signal belongs to a single, a-priori unknown, subspace out of a union of multiple candidate subspaces. We discuss in detail recent strategies for sub-Nyquist sampling in union models including low rate sampling of multiband signals, recovery of time delays from low rate samples, spectrum sensing for cognitive radio, super-resolution radar, low-rate ultrasound imaging, reduced dimensional multiuser detection and more.


Yonina C. Eldar received the B.Sc. degree in Physics in 1995 and the B.Sc. degree in Electrical Engineering in 1996 both from Tel-Aviv University (TAU), Tel-Aviv, Israel, and the Ph.D. degree in Electrical Engineering and Computer Science in 2002 from the Massachusetts Institute of Technology (MIT), Cambridge. She is currently a Professor in the Department of Electrical Engineering at the Technion - Israel Institute of Technology, Haifa, Israel. She is also a Research Affiliate with the Research Laboratory of Electronics at MIT and a Visiting Professor with the Electrical Engineering and Statistics departments at Stanford University, Stanford, CA. Dr. Eldar was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. In 2004, she was awarded the Wolf Foundation Krill Prize for Excellence in Scientific Research, in 2005 the Andre and Bella Meyer Lectureship, in 2007 the Henry Taub Prize for Excellence in Research, in 2008 the Hershel Rich Innovation Award, the Award for Women with Distinguished Contributions, the Muriel & David Jacknow Award for Excellence in Teaching, and the Technion Outstanding Lecture Award, in 2009 the Technion's Award for Excellence in Teaching, and in 2010 the Michael Bruno Memorial Award from the Rothschild Foundation. She is a member of the IEEE Signal Processing Theory and Methods technical committee and the Bio Imaging Signal Processing technical committee, and an Associate Editor for several IEEE and SIAM journals.

T-2: Elements of Network Information Theory [Slides]

Presenter: Abbas El Gamal and Young-Han Kim

Sunday, July 31, 9:30 - 13:00, [Red 8+9]


This tutorial focuses on the elementary and unified approach to coding schemes and achievability proofs developed in our upcoming book, Network Information Theory. This approach aims to make the subject more accessible to students and practitioners and to allow researchers to explore new problems in the field without "reinventing the wheel". Our approach uses typicality and several simple "universal" lemmas to prove achievability for discrete memoryless source and channel models. We show that lossless source coding is a special case of lossy source coding. We then show how the proofs for discrete memoryless models can be readily extended to their Gaussian counterparts via discretization and taking appropriate limits. We illustrate our approach by walking through the proofs of several classical coding theorems, including:

  • multiple access channel (coded time sharing),
  • degraded broadcast channel (superposition coding and simultaneous nonunique decoding),
  • channel with state (Gelfand-Pinsker coding),
  • lossy source coding with side information (Wyner-Ziv coding),
  • wiretap channel (randomized encoding),
  • relay channel (decode-forward and compress-forward), and
  • noisy networks (cutset bound and noisy network coding).


Abbas El Gamal is the Hitachi America Professor in the School of Engineering and the Director of the Information Systems Laboratory in the Department of Electrical Engineering at Stanford University. In the field of network information theory, he is best known for his seminal contributions to the relay, broadcast, and interference channels; multiple description coding; coding for noisy networks; and energy-efficient packet scheduling and throughput-delay tradeoffs in wireless networks. He is a Fellow of the IEEE and has received several honors and awards for his research contributions.

Young-Han Kim is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. His research focuses on information theory and statistical signal processing. He is a recipient of the 2008 NSF Faculty Early Career Development (CAREER) Award and the 2009 US-Israel Binational Science Foundation Bergmann Memorial Award.

T-3: Algebraic Structure in Network Information Theory [Slides]

Presenter: Michael Gastpar and Bobak Nazer

Sunday, July 31, 14:30 - 18:00, [Red 10]


In a multi-user network, the standard approach for proving that a set of rates is achievable involves methods that are primarily statistical in nature. One canonical example of this approach is Shannon's i.i.d. random coding argument. By contrast, algebraic methods are used to develop practical encoding and decoding schemes of reasonable complexity. However, an emerging body of work has demonstrated that algebraic methods also have an important role to play in establishing the fundamental capacity limits of networks. That is, in certain scenarios, structured random coding arguments have been able to achieve rates beyond what is possible using the best known i.i.d. random coding methods. This phenomenon was first noticed in 1979 by Korner and Marton for the distributed compression of the parity of two dependent sources. More recently, several groups have shown that algebraic structure can significantly enhance performance in relay networks, interference channels, distributed source coding, distributed interference cancellation, and physical layer network coding, among others. The aim of this tutorial is to provide an accessible introduction to structured random codes and their applications in network information theory. We will start with random linear codes for discrete memoryless channels and move towards random lattice codes for Gaussian channels. To develop intuition, we will first review how these methods can be used to prove capacity results in classical settings. Afterwards, we will discuss several settings in which the inherent algebraic structure of the code provides some advantages.


Michael Gastpar is an Associate Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, and a Professor at Ecole Polytechnique Federale, Lausanne, Switzerland. His research focuses on network information theory, signal processing, and neuroscience. He is serving as an Information Theory Society Distinguished Lecturer and an Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory.

Bobak Nazer is an Assistant Professor at the Department of Electrical and Computer Engineering at Boston University. His research interests are in information theory, communications, and signal processing, with applications to wireless networks and distributed reliable computation. For his dissertation research, he received the Eli Jury Award from the EECS Department at UC - Berkeley.

T-4: Geometry of error correcting codes and sphere packing

Presenter: Michael Tsfasman

Sunday, July 31, 14:30 - 18:00, [Red 8+9]


We shall look at error-correcting codes from a geometric point of view. They happen to be related with many natural geometric problems in finite spaces. Then we shall look at intrinsic relations between codes and sphere packings. The geometric counterpart of this relation is the analogy between algebraic geometry of curves and number theory.