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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.

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

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Date deposited: 09 Dec 2016 10:12
Last modified: 01 Feb 2018 17:33

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Contributors

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

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