SPCOM 2012




22-25 JULY, 2012

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                                                                                              IISc Flora and Fauna,Courtesy: Sharath Ahuja


All tutorials will be held on Sunday, 22 July 2012. There are three concurrent tutorials in the forenoon (AM) and three concurrent tutorials in the afternoon (PM). Each tutorial consists of two sessions with a break in between. All tutorials, except for T1, will be held in the JN Tata Auditorium building. Tutorial T1 alone will be held in the Golden Jubilee Hall of the ECE Department.






Adaptive Learning and Decision Making in Cognitive Radio Networks

Qing Zhao & Ananthram Swami

Golden Jubilee Hall, ECE Dept


Introduction to Random Matrix Theory

Manjunath Krishnapur &
   Rajesh Sundaresan

Seminar Hall B


Sparse Arrays, Sub-Nyquist Sampling and Applications

P.P. Vaidyanathan

Seminar Hall C


Stochastic Geometry: A New Tool for Wireless Network Analysis

Radhakrishna Ganti

Seminar Hall A


Network Coding for Cloud Storage

Alex Dimakis

Seminar Hall B


Statistics, Systems Theory and Information Theory: Applications to Modeling the Network Dynamics of Neural Processes

Todd P. Coleman


Seminar Hall C

                 AM Tutorials                                      PM Tutorials

     Session 1 :    09:30 - 11:00 am              Session 3 :      2:30 - 4:00 pm
  Break     :    11:00 - 11:30 am                Break     :      4:00 - 4:30 pm
Session 2 :    11:30am - 1:00 pm            Session 4 :      4:30 - 6:00 pm


              1.    Adaptive Learning and Decision Making in Cognitive Radio Networks

                Speakers: Qing Zhao and Ananthram Swami

          Abstract: Dynamic Spectrum Access (DSA) has emerged as a new area of research to deal with the paradox that spectrum is scarce but underutilized. Of the many approaches to DSA, spectrum overlay, best referred to as ´┐ŻOpportunistic Spectrum Access" (OSA), is perhaps the most compatible with the current spectrum management policies and legacy wireless systems, and has received the most attention. We will present a decision-theoretic framework for OSA, based on the theory of Partially Observable Markov Decision Process (POMDP) and Multi-Armed Bandit (MAB).  We tackle the design of the physical, MAC, and network layers of OSA systems, providing a relatively complete design within the framework of adaptive learning and decision theory. Such a coherent approach based on a rigorous mathematical theory allows us to systematically examine fundamental issues and inter-layer interactions in OSA networks, to characterize the optimal performance achieved by a joint design of various system parameters at different layers, and to tackle the challenge of OSA design under unknown dynamic models for which MAB theory offers a general and powerful tool in optimal sequential decision making and learning in uncertain environments.

          Presenters Bio:

        Qing Zhao received the Ph.D. degree in Electrical Engineering in 2001 from Cornell University, Ithaca, NY. In August 2004, she joined the Department of Electrical and Computer Engineering at University of California, Davis, where she is currently a Professor. Her research interests are in the general area of stochastic optimization, decision theory, and algorithmic theory in dynamic systems and communication and social networks.

  She received the 2010 IEEE Signal Processing Magazine Best Paper Award and the 2000 Young Author Best Paper Award from the IEEE Signal Processing Society. She holds the title of UC Davis Chancellor´┐Żs Fellow and received the 2008 Outstanding Junior Faculty Award from the UC Davis College of Engineering. She was a plenary speaker at the 11th IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2010. She is also a co-author of two papers that received student paper awards at ICASSP 2006 and the IEEE Asilomar Conference 2006.

         Ananthram Swami received the B.Tech. degree from IIT, Bombay; the M.S. degree from Rice University, Houston; and the Ph.D. degree from the University of Southern California (USC), all in Electrical Engineering. He has held positions with Unocal Corporation, USC, CS-3 and Malgudi Systems.

 He was a Statistical Consultant to the California Lottery, and developed a Matlab-based toolbox for non-Gaussian signal processing. He has held visiting faculty positions at INP, Toulouse, France, and has taught short courses for industry. He is currently with the US Army Research Laboratory, Adelphi, MD, where he is a Fellow. His work is in the broad area of signal processing and communications.


  2.    Introduction to Random Matrix Theory

         Speakers: Manjunath Krishnapur and Rajesh Sundaresan

         Abstract: We give a brief overview of some results on random matrices as relevant to  applications in signal processing and communications. In particular, we shall introduce Wigner and Wishart matrices and their limiting spectral distributions (Wigner's semicircle law and Marcenko-Pastur law). We shall also cover central limit theorems for linear statistics of eigenvalues. No prior knowledge of random matrix theory will be assumed.

          Presenters Bio:

        Manjunath Krishnapur received his B.Stat and M.Stat degrees from the Indian Statistical Institute, and his Ph.D. in statistics from the University of California, Berkeley. Currently, he is on the faculty of the Mathematics department at IISc Bangalore. His interests are in probability theory, in particular random matrices and random polynomials.

        Rajesh Sundaresan received his B.Tech degree from IIT Madras and his M.A. and Ph.D. degrees in electrical engineering from Princeton University. Currently, he is on the faculty of the ECE department at IISc Bangalore. His interests are in information theory and networks.


3.     Sparse Arrays, Sub-Nyquist Sampling, and Applications

         Speaker: P.P. Vaidyanathan

        Abstract: Parse sampling with nested and co-prime arrays has recently been introduced, and enjoys a number of applications in signal processing. Imagine we have a pair of uniform samplers operating simultaneously on a signal. With the sampling rates arbitrarily small, is it possible to extract any useful information about the signal at all? It turns out that under some conditions of stationarity it is possible to recover second order statistical information, which is sufficient for many applications. For example, from samples of x(t) taken at the sparse rates fs/M and fs/N (where M and N are arbitrarily large but co-prime), it is possible to estimate the autocorrelations at the dense sampling rate fs. Any application which depends only on autocorrelations will immediately benefit from such a sampling scheme. The enabling principle in these applications comes from the theory of sparse co-prime sampling. This tutorial focuses on the theory and applications of sparse sampling using various types of arrays such as the nested and coprime arrays. The sampling can be in space or time. One application in spatial sampling with sparse arrays is in the identification of multiple sources arriving from space (the DOA estimation problem). The solution to this problem depends on computation of autocorrelations. Traditionally, a sensor array with m sensors can only identify m-1 sources. By using the sparse sampling theory however, one can compute O(m2) autocorrelations, and therefore identify O(m2) sources. Both one and two dimensional applications of sparse sampling will be considered. The theory of compressive sensing, especially sparse signal reconstruction theory has interesting impact on the theory of sparse sampling. This connection will also be elaborated.

        Presenter Bio:

        P.P. Vaidyanathan has been with the California Institute of Technology since 1983. His main research interests are in digital signal processing, multirate systems, wavelet transforms, digital communications, genomic signal processing, radar signal processing, and sparse array signal processing. He has authored over 430 papers in journals and conferences, and is the author of the four books Multirate systems and filter banks (Prentice Hall, 1993), Linear Prediction Theory (Morgan and Claypool, 2008), (with Phoong and Lin) Signal Processing and Optimization for Transceiver Systems (Cambridge University Press, 2010), and (with Phoong and Lin) Filter Bank Transceivers for OFDM and DMT Systems (Cambridge University Press, 2010). He was recipient of the award for excellence in teaching at the California Institute of Technology multiple times. His papers have received awards from IEEE and from the IETE (Institute of Electronics and Telecommunications Engineers, India). Dr. Vaidyanathan is a Fellow of the IEEE, recipient of the F. E. Terman Award of the American Society for Engineering Education, past distinguished lecturer for the IEEE Signal Processing Society, recipient of the IEEE CAS Society Golden Jubilee Medal, recipient of the IEEE Signal Processing Society Technical Achievement Award, and recipient of the IEEE Signal Processing Society Education Award.


      4.     Stochastic Geometry for the Analysis and Design of Wireless Networks

              Speaker: Radhakrishna Ganti  

              Abstract: Network geometry is critical to the interference and hence plays an important role in the performance of a wireless network. Most emerging wireless systems are unplanned, decentralized and heterogeneous e.g., cognitive networks, smart grids, sensor networks and home networks. Current cellular systems are also increasingly exhibiting similar trends with the introduction of picocells and femtocells. The random node locations in these networks coupled with their decentralized nature provide us with unique challenges in terms of the analysis of interference and network performance.

 Point processes are increasingly being used to model the spatial randomness in wireless networks. Tools from stochastic geometry have been successfully applied to analyze performance metrics like SINR distributions, outage probability, transmission capacity  and ergodic rate. In this tutorial, the basic tools from stochastic geometry and point processes required for the analysis of interference in wireless networks will be introduced. Building upon these techniques, the analysis of interference in multi-antenna  ad-hoc networks, cellular networks, and multi-tier cellular networks (femtocells) will be discussed 

        Presenter Bio:

       Radhakrishna Ganti  is an Assistant Professor at the Indian Institute of Technology Madras, Chennai, India. Prior, he was a  Postdoctoral researcher in the Wireless Networking and Communications Group at UT  Austin from 2009-11.  He received his B. Tech. and M. Tech. in Electrical Engineering from the Indian Institute of Technology, Madras, and a Masters in Applied Mathematics and a Ph.D. in EE from the University of Notre Dame in 2009. His doctoral work focused on the spatial analysis of interference networks using tools from stochastic geometry. He is a co-author of the monograph Interference in Large Wireless Networks (NOW Publishers, 2008).


5.     Network Coding for Cloud Storage

        Speaker:  Alex Dimakis

        Abstract: Modern distributed storage systems often use erasure coding to introduce redundancy for high reliability. We show how network coding can surprisingly make the maintenance of such distributed erasure coded systems more efficient by orders of magnitude compared to standard Reed-Solomon codes. We survey the developing theory and practice of regenerating codes. Both lower bounds via information theoretic cut-set arguments and constructive techniques will be developed in a tutorial way. We will discuss recent implementations over Apache Hadoop (HDFS) and the Network Coding File System (NCFS) and open research directions.

        Presenter Bio:

        Alex Dimakis is an Assistant Professor at the Viterbi School of Engineering, University of Southern California since 2009. He currently holds the Colleen and Roberto Padovani Early Career Chair in Electrical Engineering. He received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree in Electrical and Computer Engineering from the National Technical University of Athens in 2003. During 2009 he was a postdoctoral scholar at the CMI center at Caltech. He received the NSF Career award in 2011, the Eli Jury dissertation award in 2008 and IEEE ComSoc Data Storage committee best paper award in 2010.  His interests include communications, coding theory, signal processing, and networking, with a current focus on distributed storage, network coding, distributed inference and message.


6.     Statistics, Systems Theory and Information Theory: Applications to Modeling the Network Dynamics of Neural Processes

        Speaker:  Todd P. Coleman

        Abstract: Recent technological and experimental advances in neuroscience, such as brain imaging, microelectrode recording and bio-sensors, have led to an unprecedented increase in the types and volume of data collected during experiments on complex neuronal systems. Such data provides growing evidence that dynamics and temporal dependencies between the system components play a dominant role in the execution of behavioral, cognitive and motor tasks, both in healthy and pathological conditions. Traditional statistic methods, based on 1st and 2nd order statistics, however, cannot explain such dynamical features and have led neuroscientists to a road block. This workshop will discuss alternative systems-based methods of modeling and estimation from data to understand neuronal network dynamics. We will also demonstrate how statistical questions of causality and dynamics at a network level are of particular importance to the neuroscience community, and how information-theoretic concepts pertaining to prediction can play a role in uncovering such dynamic network structures. A specific application of this approach will involve discovery of a wave-like interaction of neurons spiking within primary motor cortex - the direction of which is consistent with what has recently been discovered in local field potentials.  We hope to demonstrate how viewing a neural circuit as a network composed of coupled stochastic dynamic systems allows for principled analyses, interpretation, and control of neural processes.

        Presenter Bio:

        Todd P. Coleman is an Associate Professor in the Department of Bioengineering with affiliations in the Information Theory & Applications Center, the Institute of Engineering in Medicine, and the Institute for Neural Computation at UCSD. He holds B.S. degrees in electrical engineering and computer engineering from Michigan, as well as M.S. and Ph.D. degrees in electrical engineering from MIT. He was an Assistant Professor of Electrical & Computer Engineering and Neuroscience at UIUC from 2006 until 2011.  He directs the Neural Interaction Lab at UCSD where his group conducts research on flexible "tattoo electronics" for neurological monitoring, quantitative approaches to understand interacting neural signals within brains, and team decision theory approaches to design brain-computer interfaces.  His research is highly interdisciplinary, at the intersection of bio-electronics, neuroscience, and applied probability. His recent research on "tattoo electronics" has been featured in CNN, the New York Times, and Popular Science.