Abstract
A nonlinear detection technique designed for
multiple-antenna assisted receivers employed in space-division
multiple-access systems is investigated. We derive the
optimal solution of the nonlinear spatial-processing assisted
receiver for binary phase shift keying signalling, which we refer
to as the Bayesian detector. It is shown that this optimal
Bayesian receiver significantly outperforms the standard linear
beamforming assisted receiver in terms of a reduced bit error
rate, at the expense of an increased complexity, while the
achievable system capacity is substantially enhanced with the
advent of employing nonlinear detection. Specifically, when the
spatial separation expressed in terms of the angle of arrival
between the desired and interfering signals is below a certain
threshold, a linear beamformer would fail to separate them, while
a nonlinear detection assisted receiver is still capable of
performing adequately. The adaptive implementation of the optimal
Bayesian detector can be realized using a radial basis function
network. Two techniques are presented for constructing
block-data-based adaptive nonlinear multiple-antenna assisted
receivers. One of them is based on the relevance vector machine
invoked for classification, while the other on the orthogonal
forward selection procedure combined with the Fisher ratio
class-separability measure. A recursive sample-by-sample
adaptation procedure is also proposed for training nonlinear
detectors based on an amalgam of enhanced κ-means
clustering techniques and the recursive least squares algorithm.