DocumentCode :
3523309
Title :
Adaptive predistortion of nonlinear Volterra systems using Spectral Magnitude Matching
Author :
Abd-Elrady, Emad ; Gan, Li ; Kubin, Gernot
Author_Institution :
Christian Doppler Lab. for Nonlinear Signal Process., Graz Univ. of Technol., Graz
fYear :
2009
fDate :
19-24 April 2009
Firstpage :
2985
Lastpage :
2988
Abstract :
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 error between the spectral magnitudes of the output signal of the nonlinear system and the desired signal. The coefficients of the predistorter are estimated recursively using the generalized Newton iterative algorithm. A comparative simulation study with the nonlinear filtered-x least mean squares (NFxLMS) algorithm shows that the suggested SMM approach achieves much better performance but with higher computation complexity.
Keywords :
Volterra series; least mean squares methods; nonlinear systems; spectral analysis; Volterra series; adaptive predistortion; digital compensation; direct learning architecture; generalized Newton iterative algorithm; nonlinear Volterra systems; nonlinear filtered-x least mean squares; spectral magnitude matching; Adaptive signal processing; Distortion measurement; Gallium nitride; Iterative algorithms; Laboratories; Nonlinear distortion; Nonlinear systems; Oral communication; Predistortion; Recursive estimation; Adaptive systems; Volterra series; nonlinear systems; parameter estimation; spectral analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on
Conference_Location :
Taipei
ISSN :
1520-6149
Print_ISBN :
978-1-4244-2353-8
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2009.4960251
Filename :
4960251
Link To Document :
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