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Adaptive filtering using the least-mean mixed-norms algorithm and its application to echo cancellation.

Al-Naffouri, Tareq Yousef (1997) Adaptive filtering using the least-mean mixed-norms algorithm and its application to echo cancellation. Masters thesis, King Fahd University of Petroleum and Minerals.

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English Abstract

Echo is a debiliting problem for full-duplex data transmission over the telephone network and hence must be cancelled. This echo tends to divide into two distinct components which exhibit quite different characteristics. The recently proposed least-mean mixed-norms algorithm utilizes this difference to achieve a higher degree of cancellation as compared to the single-norm algorithm that is usually used. In this thesis, the least-mean mixed-norms algorithm is studied for a general pair of error nonlinerities. In particualar, the convergence of the algorithm is studied and its performance is evaluated for both correlated and independent identically-distributed inputs. The calculus of variations is then used to determine the optimum pair of nonlinearities for each input. These optimum nonlinearities are expressed in terms of the additive-noise probability density function (pdf). Approximating the pdf using the Gram-Charlier expansion provides a practical way for implementing the optimal nonlinearities. All of the above theoretical developments encompass and extend many existing results. Simulation was finally used to demonstrate the advantages of the least-mean mixed-norms algorithm over the single-norm algorithm for half-duplex data transmission.



Item Type:Thesis (Masters)
Date:June 1997
Date Type:Completion
Subjects:Electrical
Divisions:College Of Engineering Sciences > Electrical Engineering Dept
Creators:Al-Naffouri, Tareq Yousef
Committee Advisor:Bettayeb, Mammar
Committee Members:Zerguine, Azzedine and Kousa, Maan A. G. and Al-Duwaish, Hussain and Belghonaim, Adil and Fahmy, Moustafa M.
ID Code:10224
Deposited By:KFUPM ePrints Admin
Deposited On:22 Jun 2008 16:59
Last Modified:25 Apr 2011 10:25

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