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*_Notice and Invitation_*
Oral Defense of Doctoral Dissertation
The Volgenau School of Engineering, George Mason University

Weiwei Zhou
Bachelor of Science, University of Science and Technology Liaoning, 2004
Master of Science,GyeongsangNational University, 2007

  

*Computationally Efficient Equalizer Design*

July 10^th , 2014, 12:30pm-2:30pm
Engineering Building 3507

All are invited to attend.

*_Committee_*
Dr. Jill K. Nelson, Chair
Dr. Bernd-Peter Paris, Committee Member
Dr. Shih-Chun Chang, Committee Member

Dr. Jie Xu, Committee Member

*_Abstract_*

Intersymbol interference (ISI) caused by frequency selective multipath 
propagation is a primary source of distortion in wireless communication 
systems. ISI significantly degrades system performance, and hence 
channel equalization is typically employed at the receiver to mitigate 
the harmful effects of ISI. An equalizer can be designed to operate on 
either a symbol-by-symbol or sequential basis. Symbol-by-symbol based 
equalizers estimate the transmitted symbols one at a time, while 
sequential-detection based equalizers make an estimate of the full 
transmitted sequence based on the received signal over a full block of 
data. In this work, we propose computationally efficient methods to 
design both symbol-by-symbol and sequential equalizers for various 
communications scenarios.

In symbol-by-symbol schemes, we focus on the computationally efficient 
design of a maximum asymptotic efficiency (MAE) equalizer. The MAE 
equalizer achieves an attractive balance between performance and 
complexity. It minimizes bit error rate as the signal-to-noise ratio 
approaches infinity while retaining simple implementation by using a 
linear structure. However, its design requires solving quadratic 
programming problems and hence has high computational complexity. We 
propose a geometrically-inspired approach to the MAE equalizer design 
that dramatically reduces complexity. Additionally, we extend the MAE 
equalizer to applications in which the channel varies significantly with 
time by proposing a pre-equalization technique which enables the MAE 
equalizer to be designed only once despite channel variations. The 
combination of the two proposed methods simplifies the design of the MAE 
equalizer, facilitating its use for time-varying channels with longer 
delay spreads.

In sequential detection schemes, we focus on communication problems 
which involve detecting data transmitted over channels with a small 
number of sparsely spaced channel taps. Such sparse channels are present 
in applications such as underwater acoustic (UWA) communications, 
ultra-wide band (UWB) communications, and high-definition television 
(HDTV) systems. We propose a tree-search based sequential equalizer that 
considers only the significant channel coefficients. In addition, we 
consider situations in which the sparse channel is unknown and no 
training data is available. We develop a blind sequential detection 
method by incorporating a novel greedy algorithm into a tree-search 
based sequential detector. The proposed technique reduces complexity and 
yields improved performance relative to existing matching pursuit (MP) 
based methods.


A copy of this doctoral dissertation is on reserve at the Johnson Center 
Library.