Matrix computations (3rd ed.)
Handbook of Neural Network Signal Processing
Handbook of Neural Network Signal Processing
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Space-Time Coding
Intelligent Data Analysis
Letters: Gaussian moments for noisy complexity pursuit
Neurocomputing
Blind detection of orthogonal space-time block coding based on ICA schemes
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
A simple transmit diversity technique for wireless communications
IEEE Journal on Selected Areas in Communications
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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For increasing the capacity of the wireless channel, the Space -- Time Block Coding (STBC) has been proposed in the literature. The problem with such scheme is that the accurate channel state information is required. The channel is then estimated by transmitting the training sequences. Such channel estimation causes the spectral efficiency problem, i.e. the useful data rate is reduced. Likewise, the noise presence in the data affects the STBC performances. In this paper, we try to overcome these drawbacks by detecting and separating the transmitted symbols without channel estimation and by including the noise in the global model. So, the noisy compound PCA - ICA model for STBC is proposed here. Using the Bit Error Rate (BER) and Signal to Noise Ration (SNR) as criteria, the obtained simulation results show that these methods are very suitable for transmitted symbols detection and separation in the STBC context.