| Abstract |
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A new method for blind equalization is proposed which changes the cost function of the equalizer as the convergence proceeds. Motivation for this idea is given by tests of the new "uniform optimum" O/sub /spl infin///sup 2/ cost function for blind equalization proposed in Satorius and Mulligan (1993), comparing it to the more familiar O/sub 4//sup 2/ Godard-like cost. The new cost achieves better asymptotic performance than O/sub 4//sup 2/ for communications data, but has a zero tracking ability measure, this being an example of the tracking/accuracy compromise of adaptive algorithms. This suggests the use of a sliding cost function algorithm which monitors the convergence state of the equalizer. The sliding cost function algorithm is developed as a "maximum a posteriori (MAP) estimate of the blind gradient" method for blind equalization which assumes the the data fit a generalized Gaussian distribution model. The model parameters are updated at each iteration, and the algorithm adapts its cost function so as to have good tracking ability while converging, and optimal steady state performance at convergence.
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 Additional Information
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Index Terms- convergence of numerical methods; tracking; Gaussian distribution; equalisers; iterative methods; deconvolution; maximum likelihood estimation; sliding cost function algorithm; blind deconvolution; blind equalization; convergence; asymptotic performance; communications data; tracking ability; adaptive algorithms; maximum a posteriori estimate; blind gradient method; generalized Gaussian distribution model; iteration
Citation:
R.H. Lambert, C.L. Nikias,
"A sliding cost function algorithm for blind deconvolution,"
asilomar,
p. 177,
29th Asilomar Conference on Signals, Systems and Computers (2-Volume Set),
1995
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