Blind equalization based on tricepstrum and neural network

Qin Xin; Liangzhu Zhou; Jianwei Wan

doi:10.1117/12.342893

22 March 1999 Blind equalization based on tricepstrum and neural network

Qin Xin, Liangzhu Zhou, Jianwei Wan

Proceedings Volume 3722, Applications and Science of Computational Intelligence II; (1999) https://doi.org/10.1117/12.342893
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

Blind equalization for a nonminimum phase channel problem arises in digital communication. Here we develop a new method to achieve blind equalization problem for linear finite impulse response (FIR) systems, whether the systems are minimum phase or not. This new approach divides the problem into two parts. Firstly, it employs the characteristic of the linear system and take the tricepstrum method to estimate the original channel. Thus, nonminimum phase channel can be reconstructed and additive Gaussian noise will be restrained. Secondly, it utilizes the nonlinear characteristic of the neural network to establish an equalizer for the original channel. This is done by using the estimated channel as a reference system to train the neural network. The neural network can reduce the degree of model uncertainty and resist additive noise. Taking the Advantage of both linear and nonlinear systems, this scheme works well for both stationary and nonstationary cases.

Citation Download Citation

Qin Xin, Liangzhu Zhou, and Jianwei Wan "Blind equalization based on tricepstrum and neural network", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); https://doi.org/10.1117/12.342893

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