Complex channel equalization using polynomial neuron model

The Artificial Neural Networks (ANN) has been applied to channel equalization with quite promising results. Although an ANN takes time during it’s training, it generates instant results during its implementation phase. ANN are capable of performing complex non-linear mapping between their input and output space. In this paper we propose a new complex neural equalizer based on a simple model of polynomial neuron. A well-defined training procedure based on back propagation is used. The low complexity equalizer with three input nodes, three hidden nodes and one output node shows good tracking performance at even lower values of signal to noise ratio (SNR). The equalizer is tested on 4 QAM complex signals used in satellite channels.