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Adaptive nonlinear equalizer using a mixture of gaussians based on-line density estimator

Chen, H., Gong, Y., Hong, X. and Chen, S. (2014) Adaptive nonlinear equalizer using a mixture of gaussians based on-line density estimator. IEEE Transactions on Vehicular Technology, 63 (9). pp. 4265-4276. ISSN 0018-9545

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To link to this item DOI: 10.1109/TVT.2014.2313458

Abstract/Summary

This paper introduces a new adaptive nonlinear equalizer relying on a radial basis function (RBF) model, which is designed based on the minimum bit error rate (MBER) criterion, in the system setting of the intersymbol interference channel plus a co-channel interference. Our proposed algorithm is referred to as the on-line mixture of Gaussians estimator aided MBER (OMG-MBER) equalizer. Specifically, a mixture of Gaussians based probability density function (PDF) estimator is used to model the PDF of the decision variable, for which a novel on-line PDF update algorithm is derived to track the incoming data. With the aid of this novel on-line mixture of Gaussians based sample-by-sample updated PDF estimator, our adaptive nonlinear equalizer is capable of updating its equalizer’s parameters sample by sample to aim directly at minimizing the RBF nonlinear equalizer’s achievable bit error rate (BER). The proposed OMG-MBER equalizer significantly outperforms the existing on-line nonlinear MBER equalizer, known as the least bit error rate equalizer, in terms of both the convergence speed and the achievable BER, as is confirmed in our simulation study

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:36569
Uncontrolled Keywords:Adaptive nonlinear equalizer, radial basis function, minimum bit error rate, probability density function, mixture of Gaussians
Publisher:IEEE

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