This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.
Permalink
https://hdl.handle.net/2142/80992
Description
Title
Source and Channel Coding for Wireless Networks
Author(s)
Tavildar, Saurabha R.
Issue Date
2006
Doctoral Committee Chair(s)
Viswanath, Pramod
Department of Study
Electrical and Computer Engineering
Discipline
Electrical and Computer Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Engineering, Electronics and Electrical
Language
eng
Abstract
Then, motivated by applications in sensor networks, we study the problem of distributed source coding. Distributed source coding refers to the compression of correlated sources by encoders that are physically separated. The pioneering result of Slepian and Wolf states that the rate region for the lossless distributed source coding problem is as if the encoders were allowed to cooperate. We look at the natural generalization of this problem to sources with continuous alphabets. We model the sources as jointly Gaussian random variables and the distortion criterion as an average mean squared one. We are interested in obtaining the rate distortion region for this problem. We show that the natural strategy of point-to-point vector quantization followed by distributed compression by binning scheme of Slepian and Wolf is optimal in various instances of the problem. In particular, we focus on three instances of the problem. First, we characterize the rate distortion region for the two-terminal memoryless Gaussian source where the decoder is interested in two specific nonnegative linear combinations of a positively correlated vector Gaussian source. This includes the solution to the well-known separate distortion problem. Then, we model sources with memory as vector sources that are independent and identically distributed in time. We consider the vector Gaussian CEO problem where the vector observations are independent given an underlying vector random variable. We obtain an outer bound for the sum-rate of this problem that is tight for various instances of the problem including symmetric version. Finally, we characterize the rate region for an instance of the Gaussian many-help-one problem where there is an underlying Gaussian random variable that induces conditional independence between the observations.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
