Nonlinear Stationary Channel Equalization of QAM Signals using Multiplicative Neuron Model

摘要

A novel feed forward multiplicative neural network architecture with optimum number of nodes is used for adaptive channel equalization in this paper.The replacement of summation at each node by multiplication results in more powerful mapping because of its capability of processing higher-order information from training data. Performance comparison with Chebyshev neural network show that the proposed equalizer provides satisfactory results in terms of mean square error convergence curves and bit error rate performance at various levels of signal to noise ratios.