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-to-noise 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.
Chen
Sheng
S
Communication Research Group, School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. sqc@ecs.soton.ac.uk
Wolfgang
Andreas
A
Harris
Chris J
CJ
Hanzo
Lajos
L
D000465:Algorithms; D001499:Bayes Theorem; D003142:Communication; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D011846:Radio Waves