Kernel-based nonlinear beamforming construction using orthogonal forward selection with Fisher ratio class separability measure
Kernel-based nonlinear beamforming construction using orthogonal forward selection with Fisher ratio class separability measure
This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. Block-data adaptive implementation of the Bayesian beamformer is realized based on an orthogonal forward selection procedure with Fisher ratio for class separability measure.
478-481
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
May 2004
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Chen, S., Hanzo, L. and Wolfgang, A.
(2004)
Kernel-based nonlinear beamforming construction using orthogonal forward selection with Fisher ratio class separability measure.
IEEE Signal Processing Letters, 11 (5), .
Abstract
This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. Block-data adaptive implementation of the Bayesian beamformer is realized based on an orthogonal forward selection procedure with Fisher ratio for class separability measure.
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NBofsFRT.ps
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01288112.pdf
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Published date: May 2004
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 259267
URI: http://eprints.soton.ac.uk/id/eprint/259267
PURE UUID: 1e1c0c68-5ffb-46fa-8ef6-a443e07ed921
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Date deposited: 20 Apr 2004
Last modified: 18 Mar 2024 02:33
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Author:
S. Chen
Author:
L. Hanzo
Author:
A. Wolfgang
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