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-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.

• Type: Journal
• Year: 2008
• Where: TNN
• Authors: Sheng Chen, Andreas Wolfgang, Chris J. Harris, Lajos Hanzo
• Keywords: Classifier Construction Process, Symmetric Radial Basis, Symmetric Rbf Classifier, TNN 2008