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.

Download

[img] PDF 04469943.pdf - Version of Record
Download (581kB)

Description/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-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
Organisations: Southampton Wireless Group
ePrint ID: 265664
Date :
Date Event
May 2008Published
Date Deposited: 02 May 2008 08:37
Last Modified: 17 Apr 2017 19:16
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/265664

Actions (login required)

View Item View Item