Abstract
A novel equalization scheme for 16 QAM signals through a wireless ATM communication channel using Reduced-Complex Bilinear Recurrent Neural Network (R-CBLRNN) is proposed in this paper. The 16 QAM signals from a wireless ATM communication channel have severe nonlinearity and intersymbol interference due to multiple propagation paths in the channel. The R-CBLRNN equalizer is compared with the conventional equalizers including a Volterra filter equalizer, a decision feedback equalizer (DFE), and a multilayer perceptron type neural network (MLPNN) equalizer. The results show that the R-CBLRNN equalizer for 16 QAM signals gives very favorable results in both of the Mean Square Error(MSE) and the Symbol Error Rate (SER) criteria over conventional equalizers.
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