Adaptive nonlinear least bit error-rate detection for symmetrical RBF beamforming.
A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, the proposed RBF detector becomes capable of approaching the optimal Bayesian detection performance using channel-impaired training data. A novel nonlinear least bit error algorithm is derived for adaptive training of the symmetrical RBF detector based on a stochastic approximation to the Parzen window estimation of the detector output's probability density function. The proposed adaptive solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting four users with the aid of two receive antennas or seven users employing four receive antenna elements.
Chen
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S
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK. sqc@ecs.soton.ac.uk
Wolfgang
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A
Harris
C J
CJ
Hanzo
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L
D000465:Algorithms; D001499:Bayes Theorem; D003195:Computer Communication Networks; D006801:Humans; D007257:Information Theory; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted