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Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems

Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems
Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems
In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called “overloaded” multiple-antenna-aided communication systems. By exploiting the inherent symmetry property of the optimal Bayesian detector, the proposed symmetric RBF classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the RBF width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the aid of two receive antennas or seven users with four receive antenna elements. Index Terms—Classification, multiple-antenna system, orthogonal forward selection, radial basis function (RBF), symmetry.
737-745
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wolfgang, Andreas
029e48ea-f4a9-4334-8e9f-1d4b9fe1b0e3
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Wolfgang, Andreas
029e48ea-f4a9-4334-8e9f-1d4b9fe1b0e3
Harris, Chris J.
dc305347-9cb2-4621-b42f-3f9950116e0d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Chen, Sheng, Wolfgang, Andreas, Harris, Chris J. and Hanzo, Lajos (2008) Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems. IEEE Transactions on Neural Networks, 19 (5), 737-745.

Record type: Article

Abstract

In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called “overloaded” multiple-antenna-aided communication systems. By exploiting the inherent symmetry property of the optimal Bayesian detector, the proposed symmetric RBF classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the RBF width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the aid of two receive antennas or seven users with four receive antenna elements. Index Terms—Classification, multiple-antenna system, orthogonal forward selection, radial basis function (RBF), symmetry.

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Published date: 1 May 2008
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 265664
URI: http://eprints.soton.ac.uk/id/eprint/265664
PURE UUID: f0f8a276-0753-433e-8641-87d01ec06b51
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 02 May 2008 08:37
Last modified: 18 Mar 2024 02:34

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Contributors

Author: Sheng Chen
Author: Andreas Wolfgang
Author: Chris J. Harris
Author: Lajos Hanzo ORCID iD

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