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|Title: ||Adaptive Equalisation for Impulsive Noise Environments|
|Authors: ||Georgiadis, Apostolos T|
|Supervisor(s): ||Mulgrew, Bernard|
|Issue Date: ||Jul-2001|
|Publisher: ||University of Edinburgh; College of Science and Engineering; School of Engineering and Electronics|
|Abstract: ||This thesis addresses the problem of adaptive channel equalisation in environments where the
interfering noise exhibits non–Gaussian behaviour due to impulsive phenomena. The family
of alpha-stable distributions has proved to be a suitable and flexible tool for the modelling of signals with impulsive nature. However,non–Gaussian alpha–stable signals have infinite variance, and signal processing techniques based on second order moments are meaningless in such environments.
In order to exploit the flexibility of the stable family and still take advantage of
the existing signal processing tools, a novel framework for the integration of the stable model
in a communications context is proposed, based on a finite dynamic range receiver. The performance
of traditional signal processing algorithms designed under the Gaussian assumption
may degrade seriously in impulsive environments. When this degradation cannot be tolerated,
the traditional signal processing methods must be revisited and redesigned taking into account
the non–Gaussian noise statistics. In this direction, the optimum feed–forward and decision
feedback Bayesian symbol–by–symbol equalisers for stable noise environments are derived.
Then, new analytical tools for the evaluation of systems in infinite variance environments are
presented. For the centers estimation of the proposed Bayesian equaliser, a unified framework
for a family of robust recursive linear estimation techniques is presented and the underlying relationships
between them are identified. Furthermore, the direct clustering technique is studied
and robust variants of the existing algorithms are proposed. A novel clustering algorithm is also
derived based on robust location estimation. The problem of estimating the stable parameters
has been addressed in the literature and a variety of algorithms can be found. Some of these
algorithms are assessed in terms of efficiency, simplicity and performance and the most suitable
is chosen for the equalisation problem. All the building components of an adaptive Bayesian
equaliser are then put together and the performance of the equaliser is evaluated experimentally.
The simulation results suggest that the proposed adaptive equaliser offers a significant performance
benefit compared with a traditional equaliser, designed under the Gaussian assumption.
The implementation of the proposed Bayesian equaliser is simple but the computational complexity
can be unaffordable. However, this thesis proposes certain approximations which enable
the computationally efficient implementation of the optimum equaliser with negligible loss in
|Appears in Collections:||Engineering thesis and dissertation collection|
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