Neural Computation
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
Approximation by fully complex multilayer perceptrons
Neural Computation
Fundamentals of wireless communication
Fundamentals of wireless communication
Complex-Valued Neural Networks (Studies in Computational Intelligence)
Complex-Valued Neural Networks (Studies in Computational Intelligence)
Symbol decision equalizer using a radial basis functions neural network
NN'06 Proceedings of the 7th WSEAS International Conference on Neural Networks
Adaptive minimum bit error rate beamforming assisted receiver for QPSK wireless communication
Digital Signal Processing
Letters: Fully complex extreme learning machine
Neurocomputing
IEEE Transactions on Signal Processing
Fast orthogonal least squares algorithm for efficient subset modelselection
IEEE Transactions on Signal Processing
Adaptive minimum bit-error rate beamforming
IEEE Transactions on Wireless Communications
Performance Analysis of Digital Radio Links with Nonlinear Transmit Amplifiers
IEEE Journal on Selected Areas in Communications
Channel equalization using adaptive complex radial basis function networks
IEEE Journal on Selected Areas in Communications
A complex valued radial basis function network for equalization of fast time varying channels
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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We consider a fully complex-valued radial basis function (RBF) network for regression and classification applications. For regression problems, the locally regularised orthogonal least squares (LROLS) algorithm aided with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF models, is extended to the fully complex-valued RBF (CVRBF) network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully CVRBF network. The proposed fully CVRBF network is also applied to four-class classification problems that are typically encountered in communication systems. A complex-valued orthogonal forward selection algorithm based on the multi-class Fisher ratio of class separability measure is derived for constructing sparse CVRBF classifiers that generalise well. The effectiveness of the proposed algorithm is demonstrated using the example of nonlinear beamforming for multiple-antenna aided communication systems that employ complex-valued quadrature phase shift keying modulation scheme.