Chen, S., Wolfgang, A., Harris, C.J. and Hanzo, L.
Adaptive nonlinear least bit error-rate detection for symmetric RBF beamforming
Neural Networks, 21, (2-3), .
A powerful symmetrical radial basis function (RBF) aided detector is proposed for nonlinear detection in so-called rank-deficient multipleantenna 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.
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