This paper presents a method for reducing the number of weights in a time series behavioral model for a power amplifier. The least-absolute shrinkage and selection operator (Lasso) algorithm is used to reduce the kernel size, preserving the important kernels, while eliminating the less important kernels. The algorithm is evaluated on a behavioral model for a class AB amplifier, the algorithm reduces the number of weights by greater than 70% without degrading model performance by a significant amount
This letter presents the use of two heuristic search algorithms, named simulated annealing and gene...
This article discusses several different implementations of Volterra seriesmodels, and performs a fa...
This letter presents the use of two heuristic search algorithms, named simulated annealing and genet...
This paper presents a method for reducing the number of weights in a time series behavioral model fo...
When a larger than required dimension such as memory depth or order of nonlinearity, is specified d...
When a larger than required dimension such as memory depth or order of nonlinearity, is specified d...
The objective of this paper is to present an approach to behavioral modeling that can be applied to ...
Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation ...
In this paper efficient computer implementations of commonly used Volterra series based power amplif...
In this paper efficient computer implementationsof commonly used Volterra seriesbased power amplifie...
Article number 9178996We present a new formulation of the doubly orthogonal matching pursuit (DOMP) ...
This paper presents a new behavioral model for power amplifiers that accomplishes the capture of non...
This article discusses several different implementations of Volterra seriesmodels, and performs a fa...
In this paper, we present an algorithm which uses the probability information of the input signal t...
This paper presents a study oriented at reducing the computational complexity of least squares (LS) ...
This letter presents the use of two heuristic search algorithms, named simulated annealing and gene...
This article discusses several different implementations of Volterra seriesmodels, and performs a fa...
This letter presents the use of two heuristic search algorithms, named simulated annealing and genet...
This paper presents a method for reducing the number of weights in a time series behavioral model fo...
When a larger than required dimension such as memory depth or order of nonlinearity, is specified d...
When a larger than required dimension such as memory depth or order of nonlinearity, is specified d...
The objective of this paper is to present an approach to behavioral modeling that can be applied to ...
Volterra models allow modeling nonlinear dynamical systems, even though they require the estimation ...
In this paper efficient computer implementations of commonly used Volterra series based power amplif...
In this paper efficient computer implementationsof commonly used Volterra seriesbased power amplifie...
Article number 9178996We present a new formulation of the doubly orthogonal matching pursuit (DOMP) ...
This paper presents a new behavioral model for power amplifiers that accomplishes the capture of non...
This article discusses several different implementations of Volterra seriesmodels, and performs a fa...
In this paper, we present an algorithm which uses the probability information of the input signal t...
This paper presents a study oriented at reducing the computational complexity of least squares (LS) ...
This letter presents the use of two heuristic search algorithms, named simulated annealing and gene...
This article discusses several different implementations of Volterra seriesmodels, and performs a fa...
This letter presents the use of two heuristic search algorithms, named simulated annealing and genet...
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