Journal ArticleDOI
Channel Equalization Using Adaptive Lattice Algorithms
E. Satorius,S.T. Alexander +1 more
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TLDR
The orthogonalization properties of the lattice algorithms make them appear promising for equalizing channels which exhibit heavy amplitude distortion, and the number of operations per update for the adaptive lattice equalizers is linear with respect to thenumber of equalizer taps.Abstract:
In this paper, a study of adaptive lattice algorithms as applied to channel equalization is presented. The orthogonalization properties of the lattice algorithms make them appear promising for equalizing channels which exhibit heavy amplitude distortion. Furthermore, unlike the majority of other orthogonalization algorithms, the number of operations per update for the adaptive lattice equalizers is linear with respect to the number of equalizer taps.read more
Citations
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Fading channels: information-theoretic and communications aspects
TL;DR: This paper describes the statistical models of fading channels which are frequently used in the analysis and design of communication systems, and focuses on the information theory of fading channel, by emphasizing capacity as the most important performance measure.
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Adaptive equalization
TL;DR: In this article, the authors give an overview of the current state of the art in adaptive equalization and discuss the convergence and steady-state properties of least mean square (LMS) adaptation algorithms.
Adaptive equalization
TL;DR: This tutorial paper gives an overview of the current state of the art in adaptive equalization and discusses the convergence and steady-state properties of least mean-square (LMS) adaptation algorithms, including digital precision considerations, and three classes of rapidly converging adaptive equalizer algorithms.
Journal ArticleDOI
Lattice filters for adaptive processing
TL;DR: This paper presents a tutorial review of lattice structures and their use for adaptive prediction of time series, and it is shown that many of the currently used lattice methods are actually approximations to the stationary least squares solution.
References
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Stationary and nonstationary learning characteristics of the LMS adaptive filter
TL;DR: It is shown that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value.
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The complex LMS algorithm
TL;DR: A least-mean-square (LMS) adaptive algorithm for complex signals is derived where the boldfaced terms represent complex (phasor) signals and the bar above Xj designates complex conjugate.
Journal ArticleDOI
Stable and efficient lattice methods for linear prediction
TL;DR: A class of stable and efficient recursive lattice methods for linear prediction that guarantee the stability of the all-pole filter, with or without windowing of the signal, with finite wordlength computations, and at a computational cost comparable to the traditional autocorrelation and covariance methods is presented.
Journal ArticleDOI
Application of Fast Kalman Estimation to Adaptive Equalization
TL;DR: This work shows how certain "fast recursive estimation" techniques, originally introduced by Morf and Ljung, can be adapted to the equalizer adjustment problem, resulting in the same fast convergence as the conventional Kalman implementation, but with far fewer operations per iteration.