Symmetric Kernel Detector for Multiple-Antenna Aided Beamforming Systems
Symmetric Kernel Detector for Multiple-Antenna Aided Beamforming Systems
We propose a powerful symmetric kernel classifier for nonlinear detection in challenging rank-deficient multipleantenna aided communication systems. By exploiting the inherent odd symmetry of the optimal Bayesian detector, the proposed symmetric kernel classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the kernel width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the powerfull linear minimum bit error rate benchmarker, when supporting five users with the aid of three receive antennas.
2486-2491
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
2007
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Wolfgang, A.
e87811dd-7028-4ac3-90cc-62003ff22202
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hanzo, L.
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, S., Wolfgang, A., Harris, C.J. and Hanzo, L.
(2007)
Symmetric Kernel Detector for Multiple-Antenna Aided Beamforming Systems.
2007 International Joint Conference on Neural Networks, Orlando, Florida, United States.
12 - 17 Aug 2007.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
We propose a powerful symmetric kernel classifier for nonlinear detection in challenging rank-deficient multipleantenna aided communication systems. By exploiting the inherent odd symmetry of the optimal Bayesian detector, the proposed symmetric kernel classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the kernel width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the powerfull linear minimum bit error rate benchmarker, when supporting five users with the aid of three receive antennas.
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ijcnn07-1642.pdf
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Published date: 2007
Additional Information:
Event Dates: 12- 17 August 2007
Venue - Dates:
2007 International Joint Conference on Neural Networks, Orlando, Florida, United States, 2007-08-12 - 2007-08-17
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 264420
URI: http://eprints.soton.ac.uk/id/eprint/264420
PURE UUID: 58a081bb-5878-47a9-82e0-b5fd5b14f364
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Date deposited: 20 Aug 2007
Last modified: 18 Mar 2024 02:34
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Contributors
Author:
S. Chen
Author:
A. Wolfgang
Author:
C.J. Harris
Author:
L. Hanzo
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