Polynomial Series FLANN for Nonlinear Equalization

Document Type : Original Article

Authors

1 Electrical Engineering Department Shaqra University Dawadmi, Ar Riyadh, Saudi Arabia

2 Electronics and Electrical Communication Engineering Department Faculty of Electronic Engineering, Menoufia University Menouf, Egypt

Abstract

Efficient equalization for nonlinear communication channels with Additive White Gaussian Noise (AWGN) is presented. The proposed equalization is based on a Functional Link Artificial Neural Network (FLANN) structure in which the original input is nonlinearly expanded. The proposed nonlinear expansion follows a polynomial series. The nonlinearity incorporated at the output of the conventional FLANN is omitted in the proposed Polynomial Series Equalizer (PSE). Consequently, the convergence of the PSE is fast and its computational complexity is low. Moreover, explicit mathematical formula for the optimum PSE is obtained. The PSE is adapted using the fast gradient based signed Least Mean Squared (LMS). Simulations demonstrate that, the PSE vastly outperforms other FLANN based equalizers employing the Bit Error Rate (BER) metric at different nonlinear channel models and different Signal to Noise Ratios (SNR). 

Keywords

References

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Menoufia Journal of Electronic Engineering Research
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 78-82

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