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This paper considers the problem of digital predistortion of parallel Wiener-type systems using the recursive prediction error method (RPEM) and the nonlinear filtered-x least mean squares (NFxLMS) algorithms. The RPEM algorithm is used for the identification of the parallel Wiener-type system and the FIR filter that represents the inverse of the linear kernels. Then the estimate of the nonlinear...
This paper considers the problem of predistortion of nonlinear systems which are described using IIR Hammerstein models by connecting two adaptive IIR Wiener systems. The first adaptive Wiener system is a training filter connected in parallel with the nonlinear system and its coefficients are estimated recursively using the Recursive Prediction Error Method (RPEM) algorithm. The second adaptive Wiener...
This paper considers the problem of predistortion of nonlinear systems which are described using FIR Wiener models by connecting two adaptive FIR Hammerstein systems. The first adaptive Hammerstein system is a training filter connected in parallel with the nonlinear system and its coefficients are estimated recursively using the recursive prediction error method (RPEM) algorithm. The second adaptive...
Adaptive predistortion of nonlinear systems described using IIR Hammerstein models is introduced in this paper. The adaptive predistorter is modeled as an IIR Wiener system. The parameters of the linear and nonlinear blocks of the predistorter are estimated simultaneously using the nonlinear filtered-x least mean squares (NFxLMS) algorithm. The NFxLMS algorithm is derived under the assumption that...
Distortion compensation of nonlinear systems is an important topic in many practical applications. This paper concerns with linearization of nonlinear systems which can be modeled using Volterra series by connecting two adaptive nonlinear Volterra filters. The first one is a training filter connected in parallel with the nonlinear system and its kernels are estimated recursively. The second adaptive...
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