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Title: Digital predistorter design using B-spline neural network and inverse of De Boor algorithm
Authors: Chen, Sheng
Hong, Xia
Gong, Yu
Harris, Chris J.
Keywords: B-spline neural network
De Boor algorithm
Hammerstein model
Memory high power amplifier
Output saturation
Predistorter
Wiener model
Issue Date: 2013
Publisher: © IEEE
Citation: CHEN, S. ... et al, 2013. Digital predistorter design using B-spline neural network and inverse of De Boor algorithm. IEEE Transactions on Circuits and Systems I: Regular papers, 60 (6), pp. 1584-1594.
Abstract: 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.
Description: (c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Version: Accepted for publication
DOI: 10.1109/TCSI.2012.2226514
URI: https://dspace.lboro.ac.uk/2134/25661
Publisher Link: http://dx.doi.org/10.1109/TCSI.2012.2226514
ISSN: 1549-8328
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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