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Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm

Chen, S., Hong, X., Gong, Y. and Harris, C. J. (2013) Digital predistorter design using B-Spline neural network and inverse of De Boor algorithm. IEEE Transactions on Circuits and Systems Part I: fundamental theory and applications, 60 (6). pp. 1584-1594. ISSN 1057-7122

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To link to this item DOI: 10.1109/TCSI.2012.2226514

Abstract/Summary

This contribution introduces a new digital predistorter to compensate serious distortions caused by memory high power amplifiers (HPAs) which exhibit output saturation characteristics. The proposed design is based on direct learning using a data-driven B-spline Wiener system modeling approach. The nonlinear HPA with memory is first identified based on the B-spline neural network model using the Gauss-Newton algorithm, which incorporates the efficient De Boor algorithm with both B-spline curve and first derivative recursions. The estimated Wiener HPA model is then used to design the Hammerstein predistorter. In particular, the inverse of the amplitude distortion of the HPA's static nonlinearity can be calculated effectively using the Newton-Raphson formula based on the inverse of De Boor algorithm. A major advantage of this approach is that both the Wiener HPA identification and the Hammerstein predistorter inverse can be achieved very efficiently and accurately. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30200
Publisher:IEEE

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