The University of Southampton
University of Southampton Institutional Repository

Comparative performance of complex-valued B-spline and polynomial models applied to iterative frequency-domain decision feedback equalization of Hammerstein channels

Comparative performance of complex-valued B-spline and polynomial models applied to iterative frequency-domain decision feedback equalization of Hammerstein channels
Comparative performance of complex-valued B-spline and polynomial models applied to iterative frequency-domain decision feedback equalization of Hammerstein channels
Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart.
2162-237X
2872-2884
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
c46b00c1-f507-4fc4-93b6-c51ef9f9b1fd
Khalaf, Emad F.
7e92b3df-6f6c-4ca7-9602-607e890312c0
Alsaadi, Fuad E.
d55df2df-f82f-454b-adf7-c331a8192249
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
c46b00c1-f507-4fc4-93b6-c51ef9f9b1fd
Khalaf, Emad F.
7e92b3df-6f6c-4ca7-9602-607e890312c0
Alsaadi, Fuad E.
d55df2df-f82f-454b-adf7-c331a8192249
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia, Khalaf, Emad F., Alsaadi, Fuad E. and Harris, Christopher (2017) Comparative performance of complex-valued B-spline and polynomial models applied to iterative frequency-domain decision feedback equalization of Hammerstein channels. IEEE Transactions on Neural Networks and Learning Systems, 28 (12), 2872-2884. (doi:10.1109/TNNLS.2016.2609001).

Record type: Article

Abstract

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart.

Text
__filestore.soton.ac.uk_users_skr1c15_mydocuments_eprints_ECS_Chen_Comparative performance of Complex valued B-Spline.pdf - Accepted Manuscript
Download (4MB)
Text
TNNLS2017-Dec - Version of Record
Restricted to Repository staff only
Request a copy
Text
TNNLS-2016-P-5943
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 12 September 2016
e-pub ahead of print date: 23 September 2016
Published date: December 2017
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 403719
URI: http://eprints.soton.ac.uk/id/eprint/403719
ISSN: 2162-237X
PURE UUID: 393ea08a-d2a3-4d92-b19e-5a726a662c39

Catalogue record

Date deposited: 09 Dec 2016 10:12
Last modified: 15 Mar 2024 03:49

Export record

Altmetrics

Contributors

Author: Sheng Chen
Author: Xia Hong
Author: Emad F. Khalaf
Author: Fuad E. Alsaadi
Author: Christopher Harris

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.

×