The University of Southampton
University of Southampton Institutional Repository

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm

B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm
B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm
In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.
30-36
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Gong, Yu
afbb8cbf-2f34-4430-9647-a718c7b49bdc
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Hong, Xia, Gong, Yu and Chen, Sheng (2011) B-spline neural network based digital baseband predistorter solution using the inverse of De Boor algorithm. International Joint Conference on Neural Networks. 31 Jul - 05 Aug 2011. pp. 30-36 .

Record type: Conference or Workshop Item (Other)

Abstract

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

Text
IJCNN-2011-a.pdf - Version of Record
Download (458kB)

More information

Published date: August 2011
Additional Information: Event Dates: July 31 - August 5, 2011
Venue - Dates: International Joint Conference on Neural Networks, 2011-07-31 - 2011-08-05
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 272680
URI: http://eprints.soton.ac.uk/id/eprint/272680
PURE UUID: 7685f222-7c65-4f34-9de4-2b9a70f602cf

Catalogue record

Date deposited: 18 Aug 2011 08:31
Last modified: 30 Jul 2019 18:57

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×