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.
- Published Version
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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||02 May 2008 08:37|
|Last Modified:||26 Apr 2013 04:14|
|Contributors:||Chen, S. (Author)
Wolfgang, A. (Author)
Harris, C.J. (Author)
Hanzo, L. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||8|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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