Nonlinear Channel Equalization Using Wavelet Neural Network Trained Using PSO

8 Pages Posted: 10 Apr 2020

See all articles by Saikat Majumder

Saikat Majumder

National Institute of Technology Raipur

Manish Kumar Giri

National Institute of Technology Raipur

Date Written: April 10, 2020

Abstract

Wireless communication channels are increasingly being pushed into the paradigm nonlinearity and inter symbol interference (ISI) because of demand for high speed data through portable and power efficient hand held devices. ISI and nonlinearity cause severe degradation in received signal resulting in poor quality of service. In this paper, channel equalizers are designed for mitigating channel nonlinearity and ISI. We propose an improved method of training wavelet neural network-based equalizer using particle swarm optimization (PSO). Our approach consists of optimizing the translation, dilation and other weights of hidden layer to achieve improvement in bit error rate (BER) performance. Superior performance of the proposed training algorithm is established by comparing the BER with established equalization schemes in literature.

Keywords: Equalizer, wavelet, neural network, particle swarm optimization, nonlinear channel

Suggested Citation

Majumder, Saikat and Giri, Manish Kumar, Nonlinear Channel Equalization Using Wavelet Neural Network Trained Using PSO (April 10, 2020). Proceedings of the International Conference on Advances in Electronics, Electrical & Computational Intelligence (ICAEEC) 2019. Available at SSRN: https://ssrn.com/abstract=3572806 or http://dx.doi.org/10.2139/ssrn.3572806

Saikat Majumder (Contact Author)

National Institute of Technology Raipur ( email )

Raipur, 492010
India

Manish Kumar Giri

National Institute of Technology Raipur ( email )

Raipur, 492010
India

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