Optimisation of test signals for channel identification

Braithwaite, Stephen Clive (2011) Optimisation of test signals for channel identification. [Thesis (PhD/Research)] (Unpublished)

[img]
Preview
PDF (Introductory Pages)
Braithwaite_2011_front.pdf

Download (2202Kb)
[img]
Preview
PDF (Whole Thesis)
Braithwaite_2011_whole.pdf

Download (2186Kb)

Abstract

Channel estimation is required in virtually all communication systems: wireless, optical, and electrical systems. Modern mobile wireless devices undertake channel estimation repeatedly and audio communication systems of all varieties repeatedly estimate the echo-path channel so that echos can be cancelled. The channel estimation must be carried out repeatedly because the channel will vary while communication is taking place. This dissertation examines channel estimation in both acoustic and wireless cases. Echo cancellation has become ubiquitous because it is superior to other echo management techniques whenever the echo channel can successfully be estimated. Echo cancellation relies on the correct estimation and modelling of the echo channel. Despite much research into advanced echo cancellation techniques, there remains acoustic applications where the channel is too dynamic for existing echo channel estimation techniques to succeed. An echo cancellation simulation environment has been developed to facilitate the simulation of dynamic echo environments and the monitoring of the results and the internal state of echo cancellation algorithms. This environment enables the monitoring of the overall result and the internal state of the echo cancellation algorithms. Object oriented programming techniques are used to achieve the flexibility needed to do this for diff�erent algorithms, diff�erent stimulus or test signals, diff�erent background noise and diff�erent channels. While most acoustic echo channels are essentially linear, speakers can introduce non linearity to an echo channel, depending on their quality. A simpli�fed non linear echo channel model of this situation is used, and a non linear algorithm for echo cancellation developed that estimates the parameters of this model. Second order statistics are used in techniques for channel estimation in wireless systems. Their use is necessary, given the dynamic nature of wireless channels. To date, however, there have been few attempts to fully utilise second order statistics as part of channel estimation for acoustic systems. The optimal linear estimator of the channel is identif�ed that fully takes the second-order statistics of the channel variation into account. Test signals are often used in the estimation of wireless channels. They are also used in the estimation of acoustic channels. Equations characterising the optimal (in the sense of least-squares estimation error for �fixed power) test signal based on the second order statistics of the channel and of the noise are developed. The power advantage of an optimal test signal over white noise as a signal is unbounded as the channel statistics vary. Channel autocovariances may be represented by covariance matrices. The model off�ered by a covariance matrix is often too complicated for use in practice, however. A model of intermediate complexity has been developed that remains suffiently flexible to encapsulate desirable features of a channel autocovariance while being �significantly simpler to use. The simplified model of a channel autocovariance has been used to randomly generate autocovariance matrices that are suitable for use in the testing the method for fi�nding optimal test signals.


Statistics for USQ ePrint 19914
Statistics for this ePrint Item
Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Doctor of Philosophy (PhD) thesis.
Depositing User: ePrints Administrator
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 27 Oct 2011 06:11
Last Modified: 20 Jul 2014 04:38
Uncontrolled Keywords: optimal; echo channel; autocovariance; constraint; test signal; linear
Fields of Research (FOR2008): 09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080204 Mathematical Software
01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010303 Optimisation
URI: http://eprints.usq.edu.au/id/eprint/19914

Actions (login required)

View Item Archive Repository Staff Only