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This research addresses the problem inter-symbol interference (ISI) using equalization techniques for time dispersive channels with additive white Gaussian noise (AWGN). The channel equalizer is modelled as a non-linear Multilayer Perceptron (MLP) structure. The Back Propagation (BP) algorithm is used to optimize the synaptic weights of the equalizer during the training mode. In the typical BP algorithm, the error signal is propagated from the output layer to the input layer while the learning rate parameter is held constant. In this study, the BP algorithm is modified so as to allow for the learning rate to be variable at each iteration and this achieves a faster convergence. The proposed algorithm is used to train the MLP based decision feedback equalizer (DFE) for time dispersive ISI channels. The equalizer is tested for a random input sequence of BPSK signals and its performance analysed in terms of the Bit Error Rates and speed of convergence. Simulation results show that the proposed algorithm improves the Bit Error Rate (BER) and rate of convergence.

Adaptive Channel Equalization using Multilayer Perceptron Neural Networks with variable learning rate parameter