Nonlinear Channel Equalization Using Wavelet Neural Network Trained Using PSO

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.