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Complex-valued B-spline neural network and its application to iterative frequency-domain decision feedback equalization for Hammerstein communication systems

Complex-valued B-spline neural network and its application to iterative frequency-domain decision feedback equalization for Hammerstein communication systems
Complex-valued B-spline neural network and its application to iterative frequency-domain decision feedback equalization for Hammerstein communication systems
Complex-valued (CV) B-spline neural network approach offers a highly effective means for identification and inversion of Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. In this paper, we review the optimality of CV B-spline neural network approach and demonstrate its excellent approximation capability for a real-world application. More specifically, we develop a CV B-spline neural network based approach for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based NIFDDFE is demonstrated in a simulation study.
4097 - 4104
IEEE
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Khalaf, Emad
1ee91105-94c9-4cbf-a565-108aab6ba7ad
Alsaad, Fuad E.
3981cd7f-0385-4364-a3af-d967557f2a6a
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Khalaf, Emad
1ee91105-94c9-4cbf-a565-108aab6ba7ad
Alsaad, Fuad E.
3981cd7f-0385-4364-a3af-d967557f2a6a
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, Sheng, Hong, Xia, Khalaf, Emad, Alsaad, Fuad E. and Harris, Christopher (2016) Complex-valued B-spline neural network and its application to iterative frequency-domain decision feedback equalization for Hammerstein communication systems. In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE. 4097 - 4104 . (doi:10.1109/IJCNN.2016.7727733).

Record type: Conference or Workshop Item (Paper)

Abstract

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identification and inversion of Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. In this paper, we review the optimality of CV B-spline neural network approach and demonstrate its excellent approximation capability for a real-world application. More specifically, we develop a CV B-spline neural network based approach for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based NIFDDFE is demonstrated in a simulation study.

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More information

Accepted/In Press date: 20 February 2016
e-pub ahead of print date: 3 November 2016
Published date: 2016
Venue - Dates: IJCNN 2016: International Joint Conference on Neural Networks, Canada, 2016-07-24 - 2016-07-29
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 398232
URI: http://eprints.soton.ac.uk/id/eprint/398232
PURE UUID: 944a3af4-e9d3-4c78-90c9-f8d2b8ca93fa

Catalogue record

Date deposited: 22 Jul 2016 08:43
Last modified: 16 Dec 2019 18:25

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