Add to ORKGIn equalization and deconvolution tasks, the correlated nature of the input signal slows the convergence speeds of the least-mean-square (LMS) and other stochastic gradient adaptive filters. Prewhitenin
This paper discusses linear inverse filtering (deconvolution) from a stochastic signal processing po...
In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is ...
This paper studies the convergence properties of a self-organizing map (SOM) equalizer. The transmit...
In equalization and deconvolution tasks, the correlated nature of the input signal slows the converg...
Abstract—Mean squared error (MSE) has been the dominant criterion in adaptive filter theory. A major...
The addressed blind decision feedback equalizer (DFE) reverses the classical order of its feed-forwa...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
This paper describes an efficient realization of an adaptive singlechannel blind deconvolution algor...
In some recent papers new algorithms for blind adaptive equalization were proposed. These algorithms...
Adaptive filters can be used for channel equalization and estimation. When a signal goes through an ...
The LMS algorithm invented by Widrow and Hoff in 1959 is the simplest, most robust, and one of the m...
Journal ArticleAbstract-Convergence analysis of stochastic gradient adaptive filters using the sign ...
In general the least mean squares adaptive finite impulse response (FIR) filter converges more slowl...
Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are po...
An equalizer is an adaptive filter that compensates for the non-ideal characteristics of a communica...
This paper discusses linear inverse filtering (deconvolution) from a stochastic signal processing po...
In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is ...
This paper studies the convergence properties of a self-organizing map (SOM) equalizer. The transmit...
In equalization and deconvolution tasks, the correlated nature of the input signal slows the converg...
Abstract—Mean squared error (MSE) has been the dominant criterion in adaptive filter theory. A major...
The addressed blind decision feedback equalizer (DFE) reverses the classical order of its feed-forwa...
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appe...
This paper describes an efficient realization of an adaptive singlechannel blind deconvolution algor...
In some recent papers new algorithms for blind adaptive equalization were proposed. These algorithms...
Adaptive filters can be used for channel equalization and estimation. When a signal goes through an ...
The LMS algorithm invented by Widrow and Hoff in 1959 is the simplest, most robust, and one of the m...
Journal ArticleAbstract-Convergence analysis of stochastic gradient adaptive filters using the sign ...
In general the least mean squares adaptive finite impulse response (FIR) filter converges more slowl...
Adaptive filters that self-adjust their transfer functions according to optimizing algorithms are po...
An equalizer is an adaptive filter that compensates for the non-ideal characteristics of a communica...
This paper discusses linear inverse filtering (deconvolution) from a stochastic signal processing po...
In the literature, the convolutional noise obtained at the output of a blind adaptive equalizer, is ...
This paper studies the convergence properties of a self-organizing map (SOM) equalizer. The transmit...