Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Performance evaluation of adaptive filtering algorithms for the mobile communication environment
Performance evaluation of adaptive filtering algorithms for the mobile communication environment
IEEE Transactions on Signal Processing
Adaptive recovery of a chirped signal using the RLS algorithm
IEEE Transactions on Signal Processing
Non-Wiener solutions for the LMS algorithm-a time domain approach
IEEE Transactions on Signal Processing
Nonlinear effects in LMS adaptive equalizers
IEEE Transactions on Signal Processing
A performance bound for the LMS estimator
IEEE Transactions on Information Theory
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This paper investigates the nonlinear effects of the Least Mean Square (LMS) adaptive predictor. Traditional analysis of the adaptive filter ignores the statistical dependence among successive tap-input vectors and bounds the performance of the adaptive filter by that of the finite-length Wiener filter. It is shown that the nonlinear effects make it possible for an adaptive transversal prediction filter to significantly outperform the finite-length Wiener predictor. An approach is derived to approximate the total steady-state Mean Square Error (MSE) for LMS adaptive predictors with stationary or chirped input signals. This approach shows that, while the nonlinear effect is small for the one-step LMS adaptive predictor, it increases in magnitude as the prediction distance is increased. We also show that the nonlinear effect of the LMS adaptive predictor is more significant than that of the Recursive Least Square adaptive predictor.