Director of Research (if dissertation) or Advisor (if thesis)
Singer, Andrew C.
Doctoral Committee Chair(s)
Singer, Andrew C.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
communications
Markov random field
channel estimation
channel modeling
gaussian message passing
Abstract
We consider the problem of channel modeling and channel estimation.
The widely used wide sense stationary uncorrelated scattering model
for the communications channel neglects correlations between different
multipath arrivals, but this seems to oversimplify the real channel
in many cases. One example is the underwater acoustic channel,
whose impulse response is fairly continuous in delay and hence indeed
exhibits a certain correlation structure in delay.
To address this shortcoming we introduce a novel channel model that
is based on a Gaussian Markov random field (MRF) for the complex channel gains.
This graphical model is used to capture the local nature of the
statistical dependencies (in time and space) of the channel taps.
In order for the MRF
model to fit the actual physical channel well, its parameters must be adapted
appropriately.
Our approach is to find the maximum likelihood (ML) estimate of theses parameters based
on given observations. Once these parameters are known the MRF
model can then either be used for channel estimation directly or it can be embedded
into an iterative (turbo) receiver, where it is expected to improve the
data estimation performance significantly as the parameterized MRF carries prior knowledge on the channel.
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