Channel Equalization Using Adaptive Lattice Algorithms

Satorius, E.; Alexander, S.

doi:10.1109/tcom.1979.1094477

Cited by 158 publications

(24 citation statements)

References 18 publications

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“…The basic idea in decision-feedback equalization is that once an information symbol has been detected, the ISI that it causes on future symbols may be estimated and subtracted out prior to symbol detection. The DFE may be realized either in the direct form or as a lattice [223], [471], [504], [505]. The direct-form structure of the DFE is illustrated in Fig.…”

Section: B Equalization Methodsmentioning

confidence: 99%

Fading channels: information-theoretic and communications aspects

Biglieri

Proakis

Shamai³

1998

IEEE Trans. Inform. Theory

1,683

1,342

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Abstract-In this paper we review the most peculiar and interesting information-theoretic and communications features of fading channels. We first describe the statistical models of fading channels which are frequently used in the analysis and design of communication systems. Next, we focus on the information theory of fading channels, by emphasizing capacity as the most important performance measure. Both single-user and multiuser transmission are examined. Further, we describe how the structure of fading channels impacts code design, and finally overview equalization of fading multipath channels.

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Section: B Equalization Methodsmentioning

confidence: 99%

Fading channels: information-theoretic and communications aspects

Biglieri

Proakis

Shamai³

1998

IEEE Trans. Inform. Theory

1,683

1,342

View full text Add to dashboard Cite

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“…The above orthogonality properties have applications in adaptive filtering, specifically in improving the convergence of adaptive filters (Satorius and Alexander, 1979;Haykin, 2002). The process of generating the above set of orthogonal signals from the random process x(n) has also been interpreted as a kind of Gram--Schmidt orthogonalization (Haykin, 2002).…”

Section: Orthogonality Of the Optimal Prediction Errorsmentioning

confidence: 99%

The Theory of Linear Prediction

Vaidyanathan

2007

Synthesis Lectures on Signal Processing

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Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. This has enabled detailed discussion of a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts which cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications. The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. The text is self-contained for readers with introductory exposure to signal processing, random processes, and the theory of matrices, and a historical perspective and detailed outline are given in the first chapter. KEYWORDSLinear prediction theory, vector linear prediction, linear estimation, filtering, smoothing, line spectral processes, Levinson's recursion, lattice structures, autoregressive models vii Preface Linear prediction theory has had a profound impact in the field of digital signal processing. Although the theory dates back to the early 1940s, its influence can still be seen in applications today. The theory is based on very elegant mathematics and leads to many beautiful insights into statistical signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, I have focused on linear prediction in this book. This has enabled me to discuss in detail a number of issues that are normally not found in texts. For example, the theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes. This focus and its small size make the book different from many excellent texts that cover the topic, including a few that are actually dedicated to linear prediction. There are several examples and computer-based demonstrations of the theory. Applications are mentioned wherever appropriate, but the focus is not on the detailed development of these applications.The writing style is meant to be suitable for self-study as well as for classroom use at the senior and first-year graduate levels. Indeed, the material here emerged from classroom lectures that I had given over the years at the California Institute of Technology. So, the text is self-contained for readers with introductory exposure to signal processing, r...

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“…For example, the stability and lack of sensitivity of these filters to roundoff errors are a direct consequence of the losslessness property of the scattering medium [4], [42]. These properties, as well as the modularity and pipelinability of ladder filters have motivated their widespread use for adaptive equalization [43], speech processing [12], t44], and spectral estimation [14], [45].…”

Section: E[v(t)v(s)] = 6(t-s) (77b)mentioning

confidence: 99%

The Schur algorithm and its applications

Yagle

Levy²

1985

Acta Appl Math

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The Schur algorithm and its times-domain counterpart, the fast Cholseky recursions, are some efficient signal processing algorithms which are well adapted to the study of inverse scattering problems.These algorithms use a layer stripping approach to reconstruct a lossless scattering medium described by symmetric two-component wave equations which model the interaction of right and left propagating waves.In this paper, the Schur and fast Cholesky recursions are presented and are used to study several inverse problems such as the reconstruction of nonuniform lossless transmission lines, the inverse problem for a layered acoustic medium, and the linear least-squares estimation of stationary stochastic processes. The inverse scattering problem for asymmetric two-component wave equations corresponding to lossy media is also examined and solved by using two coupled sets of Schur recursions. This procedure is then applied to the inverse problem for lossy transmission lines.-3-

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Channel Equalization Using Adaptive Lattice Algorithms

Cited by 158 publications

References 18 publications

Fading channels: information-theoretic and communications aspects

Fading channels: information-theoretic and communications aspects

The Theory of Linear Prediction

The Schur algorithm and its applications

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