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

Chen, S., Wolfgang, A., Harris, C.J. and Hanzo, L. (2008) Symmetric RBF classifier for nonlinear detection in multiple-antenna aided systems IEEE Transactions on Neural Networks, 19, (5), pp. 737-745.

Record type: Article


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-tonoise 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: May 2008
Organisations: Southampton Wireless Group


Local EPrints ID: 265664
PURE UUID: f0f8a276-0753-433e-8641-87d01ec06b51

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Date deposited: 02 May 2008 08:37
Last modified: 18 Jul 2017 07:24

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

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