Equalization of 16 QAM signals with reduced bilinear recurrent neural network

Authors:
Dong-Chul Park;Yunsik Lee
Affiliations:
Center for Intelligent Image Processing Systems Research, Myong Ji University, Korea;Digital Convergence R & D Center, Korea Electronics Tech. Inst., Korea
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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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.