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Digital compensation of nonlinear systems is an important topic in many practical applications. This paper considers the problem of predistortion of nonlinear systems described using Volterra series by connecting in tandem an adaptive Volterra predistorter. The suggested direct learning architecture (DLA) approach utilizes the spectral magnitude matching (SMM) method that minimizes the sum squared...
Predistortion of parallel wiener-type systems is considered in this paper. The predistorter is first modeled as a Volterra series. In order to reduce computation complexity, a simplified predistorter model constructed using adaptive FIR filters is proposed. The coefficients of these two suggested predistorters are estimated using the nonlinear filtered-x least mean squares (NFtimesLMS) algorithm based...
Linearization of nonlinear systems is a very important topic in many practical applications. The linearization scheme which was suggested in for Volterra systems using adaptive linear and nonlinear FIR filters is considered in this paper. The coefficients of these filters can be recursively estimated using the Least Mean Squares (LMS) algorithm. In this paper, the Recursive Prediction Error Method...
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...
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...
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