The University of Southampton
University of Southampton Institutional Repository

Symmetric Kernel Detector for Multiple-Antenna Aided Beamforming Systems

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
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. pp. 2486-2491 .

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.

Text
ijcnn07-1642.pdf - Other
Download (140kB)
Text
symclaP.pdf - Other
Download (608kB)

More information

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
ORCID for L. Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 20 Aug 2007
Last modified: 18 Mar 2024 02:34

Export record

Contributors

Author: S. Chen
Author: A. Wolfgang
Author: C.J. Harris
Author: L. Hanzo ORCID iD

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.

×