TUTORIALS
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.
Tutorials 
Title 
Presenters 
Venue 
T1AM 
Adaptive Learning and
Decision Making in Cognitive Radio Networks 
Qing Zhao & Ananthram Swami 
Golden Jubilee
Hall, ECE Dept 
T2AM 
Introduction to Random Matrix
Theory 
Manjunath Krishnapur &
Rajesh Sundaresan 
Seminar Hall B 
T3AM 
Sparse Arrays, SubNyquist
Sampling and Applications 
P.P. Vaidyanathan 
Seminar Hall C 
T4PM 
Stochastic Geometry: A New
Tool for Wireless Network Analysis 
Radhakrishna Ganti 
Seminar Hall A 
T5PM 
Network Coding for Cloud
Storage 
Alex Dimakis 
Seminar Hall B 
T6PM 
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 �Opportunistic 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 decisiontheoretic framework for
OSA, based on the theory of Partially Observable Markov Decision
Process (POMDP) and MultiArmed 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 interlayer
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�s 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
coauthor 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, CS3 and Malgudi Systems.
He was a Statistical Consultant to the California Lottery, and
developed a Matlabbased toolbox for nonGaussian 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 MarcenkoPastur 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, SubNyquist Sampling, and Applications
Speaker:
P.P. Vaidyanathan
Abstract: Parse
sampling with nested and coprime 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 coprime), 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 coprime 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
m1 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 multiantenna adhoc networks, cellular
networks, and multitier 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 200911.
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 coauthor 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 ReedSolomon codes. We survey the
developing theory and practice of regenerating codes. Both lower
bounds via information theoretic cutset 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 biosensors,
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
systemsbased 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 informationtheoretic 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 wavelike 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
braincomputer interfaces. His research is highly
interdisciplinary, at the intersection of bioelectronics,
neuroscience, and applied probability. His recent research on
"tattoo electronics" has been featured in CNN, the New York
Times, and Popular Science.
