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

Digital predistorter design using B-spline neural network and inverse of De Boor algorithm
Digital predistorter design using B-spline neural network and inverse of De Boor algorithm
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.
1549-8328
1584-1594
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia, Gong, Yu and Harris, Chris J. (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), 1584-1594. (doi:10.1109/TCSI.2012.2226514).

Record type: Article

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.

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Published date: June 2013
Organisations: Southampton Wireless Group

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Local EPrints ID: 353055
URI: http://eprints.soton.ac.uk/id/eprint/353055
ISSN: 1549-8328
PURE UUID: ceb7e866-546f-44d7-9963-7e3e81565c91

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Date deposited: 03 Jun 2013 10:58
Last modified: 19 Jul 2019 21:35

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Contributors

Author: Sheng Chen
Author: Xia Hong
Author: Yu Gong
Author: Chris J. Harris

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