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.

Sex differences in abstract ability of volunteers and nonvolunteers for concept learning experiments

The primary aim of this study was to assess sex differences in intelligence, particularly abstract thinking ability, of 280 volunteers and nonvolunteers for concept learning experiments. A volunteer was defined as one who actually participated in the experiment. The results showed that male volunteers had significantly higher abstract thinking scores than male nonvolunteers but all other comparisons were nonsignificant. The present results underscore the necessity for controlling for differences in abstract thinking and sex in volunteers for conceptual studies.