Journal ArticleDOI
Performance advantage of complex LMS for controlling narrow-band adaptive arrays
L. Horowitz,K. Senne +1 more
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TLDR
In this article, a comprehensive analysis of the mean-squared error (MSE) of adaptation for LMS is presented, based on the method developed in the 1968 dissertation by K. D. Senne, and it represents the most complete treatment of the subject published to date.Abstract:
In narrow-band adaptive-array applications, the mean-square convergence of the discrete-time real least mean-square (LMS) algorithm is slowed by image-frequency noises generated in the LMS loops. The complex LMS algorithm proposed by Widrow et aL is shown to eliminate these noises, yielding convergence of the mean-squared error (MSE) at slightly over twice the rate. This paper includes a comprehensive analysis of the MSE of adaptation for LMS. The analysis is based upon the method developed in the 1968 dissertation by K. D. Senne, and it represents the most complete treatment of the subject published to date.read more
Citations
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Journal ArticleDOI
30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
Bernard Widrow,Michael A. Lehr +1 more
TL;DR: The history, origination, operating characteristics, and basic theory of several supervised neural-network training algorithms (including the perceptron rule, the least-mean-square algorithm, three Madaline rules, and the backpropagation technique) are described.
Journal ArticleDOI
Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations
TL;DR: This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them.
Book
Adaptation, Learning, and Optimization Over Networks
TL;DR: The limits of performance of distributed solutions are examined and procedures that help bring forth their potential more fully are discussed and a useful statistical framework is adopted and performance results that elucidate the mean-square stability, convergence, and steady-state behavior of the learning networks are derived.
Journal ArticleDOI
Adaptive IIR filtering
TL;DR: In this article, an overview of several methods, filter structures, and recursive algorithms used in adaptive infinite-impulse response (IIR) filtering is presented, and several important issues associated with adaptive IIR filtering, including stability monitoring, the SPR condition, and convergence are addressed.
Adaptive Networks
TL;DR: Under reasonable technical conditions on the data, the adaptive networks are shown to be mean square stable in the slow adaptation regime, and their mean square error performance and convergence rate are characterized in terms of the network topology and data statistical moments.
References
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Book ChapterDOI
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.
Journal ArticleDOI
Adaptive antenna systems
TL;DR: In this article, a simple adaptive technique based on the least-mean-squares (LMS) algorithm is proposed to adjust the variable weights of a signal processor by forming appropriate nulls in the antenna directivity pattern.
Journal ArticleDOI
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
On a moment theorem for complex Gaussian processes
TL;DR: A general theorem is provided for the moments of a complex Gaussian video process that states that an n th order central product moment is zero if n is odd and is equal to a sum of products of covariances when n is even.