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

Adaptive nonlinear equalizer using a mixture of Gaussians based on-line density estimator
Adaptive nonlinear equalizer using a mixture of Gaussians based on-line density estimator
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
adaptive nonlinear equalizer, minimum bit error rate (mber, mixture of gaussians, probability density function (pdf), radial basis function (rbf)
0018-9545
4265-4276
Chen, Hao
a6d9bdfb-0a77-43c7-93ae-659fd9e1623c
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, Hao
a6d9bdfb-0a77-43c7-93ae-659fd9e1623c
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

(2014) Adaptive nonlinear equalizer using a mixture of Gaussians based on-line density estimator. IEEE Transactions on Vehicular Technology, 63 (9), 4265-4276.

Record type: Article

Abstract

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

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Published date: November 2014
Keywords: adaptive nonlinear equalizer, minimum bit error rate (mber, mixture of gaussians, probability density function (pdf), radial basis function (rbf)
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 371592
URI: http://eprints.soton.ac.uk/id/eprint/371592
ISSN: 0018-9545
PURE UUID: b9b31fc1-79f4-4c24-be05-da554371ba3e

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Date deposited: 10 Nov 2014 13:56
Last modified: 17 Jul 2017 21:48

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