Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 2007.04a
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- Pages.3-5
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- 2007
A Recurrent Neural Network Training and Equalization of Channels using Sigma-point Kalman Filter
시그마포인트 칼만필터를 이용한 순환신경망 학습 및 채널등화
Abstract
This paper presents decision feedback equalizers using a recurrent neural network trained algorithm using extended Kalman filter(EKF) and sigma-point Kalman filter(SPKF). EKF is propagated, analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the Gaussian random variable. The SPKF addresses this problem by using a deterministic sampling approach. The features of the proposed recurrent neural equalizer And we investigate the bit error rate(BER) between EKF and SPKF.
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