Phase Self-amending Blind Equalization Algorithm Using Feedforward Neural Network for High-Order QAM Signals in Underwater Acoustic Channels

Authors:
Yasong Luo;Zhong Liu;Pengfei Peng;Xuezhi Fu
Affiliations:
Electronics Engineering College, Naval University of Engineering, Wuhan, China 430033;Electronics Engineering College, Naval University of Engineering, Wuhan, China 430033;Electronics Engineering College, Naval University of Engineering, Wuhan, China 430033;Electronics Engineering College, Naval University of Engineering, Wuhan, China 430033
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Nonlinear blind equalization schemes using complex-valued multilayer feedforward neural networks

IEEE Transactions on Neural Networks

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Abstract

Complex-valued and non-constant modulus signals are widely used in modern high-speed underwater acoustic communication systems. Based on this environment, a complex-valued blind equalization algorithm using feedforward neural network is brought forward. Aiming at the defects that traditional constant modulus equalization algorithm can't rectify the phase deflection, the cost function is reformed and also a new modified constant modulus algorithm is given. Besides, the new algorithm is improved by introducing the square decision technique to achieve better convergence speed and less gurgitation. The results of simulation show that this new equalization algorithm not only has the ability of phase self-amending, but also performs better than traditional algorithm in the ability and speed of convergence in high order QAM communication systems.