Multilayer Perceptrons based Blind Equalization

N. Zhang, X. Wang, H. Lin, J. Lu, and T. Yahagi (Japan)


estimation, blind equalization, nonlineardistortion, multilayer perceptron (MLP)


This paper considers the problem of blind equalization in digital communication systems by using multilayer perceptrons (MLPs). A signal suffers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, novel equalizer structures utilizing neural computation have been developed for compensating for nonlinear channel distortion. MLP is a kind of neural network with one or more hidden layers. In this paper, a fully connected three layers MLP is presented for the equalization of QPSK signals in the presence of inter symbol in terference, additive white Gaussian noise and nonlinear distortions. The network outputs provide an estimation of the source symbols. Simulation results demonstrate the effectiveness of the proposed method compared with Bussgang method.

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