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Blind adaptive equalization using cost function
Blind adaptive equalization using cost function
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机译:使用代价函数的盲自适应均衡
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摘要
A technique for the blind equalization of digital communications channels relies on the iterative minimization of a cost function known as the J-divergence between a known or assumed probability density function (PDF) of the source data signal and an estimated PDF of a receiver decision output signal derived from the equalizer output signal by minimum-distance mapping. The J-divergence function is defined in terms of the Kullback-Leibler distance between the two PDFs. Minimization is achieved by continually updating both an equalizer tap coefficient vector and the estimated PDF of the decision output signal using a stochastic gradient algorithm applied to the J-divergence cost function.
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