Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Digital Beamforming in Wireless Communications
Digital Beamforming in Wireless Communications
Third-Generation Systems and Intelligent Wireless Networking: Smart Antennas and Adaptive Modulation
Third-Generation Systems and Intelligent Wireless Networking: Smart Antennas and Adaptive Modulation
Adaptive Wireless Transceivers: Turbo-Coded, Turbo-Equalised and Space-Time Coded TDMA, CDMA, MC-CDMA and Ofdm Systems
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Adaptive minimum-BER linear multiuser detection for DS-CDMA signalsin multipath channels
IEEE Transactions on Signal Processing
Adaptive minimum bit-error rate beamforming
IEEE Transactions on Wireless Communications
Support vector machine multiuser receiver for DS-CDMA signals in multipath channels
IEEE Transactions on Neural Networks
The relevance vector machine technique for channel equalization application
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
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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 out-performs 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.