Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS
Volume 19, Issue 5, Pages 737-745Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2007.911745
Keywords
classification; multiple-antenna system; orthogonal forward selection; radial basis function (RBF); symmetry
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Funding
- European Union
- EPSRC [EP/D056691/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/D056691/1] Funding Source: researchfish
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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-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.
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