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 Transaction on Neural Networks, 19, (5), 737-745.


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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.

Item Type: Article
ISSNs: 1045-9227
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 265664
Accepted Date and Publication Date:
May 2008Published
Date Deposited: 02 May 2008 08:37
Last Modified: 31 Mar 2016 14:11
Further Information:Google Scholar

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