Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Dynamics and algorithms for stochastic search
Dynamics and algorithms for stochastic search
Decomposition of quantics in sums of powers of linear forms
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
On-line learning and stochastic approximations
On-line learning in neural networks
High-order contrasts for independent component analysis
Neural Computation
Restructuring sparse high dimensional data for effective retrieval
Proceedings of the 1998 conference on Advances in neural information processing systems II
Blind source separation via the second characteristic function
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced ICA-based receivers for block fading DS-CDMA channels
Signal Processing
On-line learning in changing environments with applications in supervised and unsupervised learning
Neural Networks - Computational models of neuromodulation
A blind signal separation method for multiuser communications
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixture of sources via anindependent component analysis
IEEE Transactions on Signal Processing
Adaptive unsupervised extraction of one component of a linear mixture with a single neuron
IEEE Transactions on Neural Networks
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
IEEE Transactions on Signal Processing
ICA with time-varying convergence factor and its application in communications
ICCOM'06 Proceedings of the 10th WSEAS international conference on Communications
Hi-index | 0.00 |
The fast fixed-point independent component analysis (ICA) algorithm has been widely used in various applications because of its fast convergence and superior performance. However, in a highly dynamic environment, real-time adaptation is necessary to track the variations of the mixing matrix. In this scenario, the gradient-based online learning algorithm performs better, but its convergence is slow, and depends on a proper choice of convergence factor. This paper develops a gradient-based optimum block adaptive ICA algorithm (OBA/ICA) that combines the advantages of the two algorithms. Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions, which is particularly useful in mobile communications.