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Transcript
Tracking and Equalizing Channels in Communication
Systems and in Data Compression
Azadeh Vosoughi
Department of Electrical and Computer Engineering at the
University of Rochester
Abstract
In this talk we address two problems that arise in digital
communication systems. The first problem deals with channel
tracking and training design for communication over
broadband wireless channels with antenna arrays at the
transmitter and the receiver. The second problem is
concerned with lossy compression of an analog information
source, with the knowledge of another correlated source
available at the decoder.
Coping with channel uncertainty is a challenging issue in
wireless communications. Channel estimation before symbol
detection, through transmitting known training symbols, is
a common technique employed in most today's communication
systems. However, training designs in these systems are
primarily heuristic. From theoretical point of view it is
crucial to explore training designs that cause the minimum
overhead and utilize the transmit resources in the most
efficient manner. Considering two receiver architectures,
namely (i) sequential channel and symbol estimation, and
(ii) joint channel and symbol estimation, and adopting the
general transmission framework of affine precoding, we
investigate optimal training designs, from both estimation
theoretic and information theoretic perspectives. Our study
shows that the design guidelines depend on the receiver
structure: while for sequential channel and symbol
estimation training and data must span orthogonal
subspaces, no such orthogonality constraint is required for
joint channel and symbol estimation.
The emerging technology of wireless sensor networks has
made distributed source coding (DSC) and data compression
vibrant fields of research. DSC refers to the compression
of multiple correlated sources that do not communicate with
each other. Source coding with side information is a
platform that facilitates removing the redundancy in the
measured data by sensors, and thus enables efficient
communication between sensors and the fusion center (e.g.,
the center to which all sensors send their messages). For
correlated vector sources x,y whose correlation can be
modelled as linear, we propose low complexity compression
algorithms for encoding x with side information y at the
decoder. The novelty of our scheme is to utilize channel
equalization and bit loading, techniques commonly used in
wireless communication to combat the distortion effect of
the channel, to solve a vector source coding problem.
Speaker Biography
Azadeh Vosoughi received the B.S. degree from Sharif
University of Technology, Tehran, Iran, in February 1997,
and the M.S. degree from Worcester Polytechnic Institute,
Worcester, MA, in August 2001, an the Ph.D. degree from
Cornell University, Ithaca, NY, in May 2006, all in
electrical engineering. Since July 2006 she has been an
Assistant professor with the Department of Electrical and
Computer Engineering at the University of Rochester,
Rochester, NY. Her general interests lie in the area of
statistical signal processing, wireless MIMO communication
systems, and distributed source coding.