Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Multiuser Detection
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Maximum Likelihood Approach to Nonlinear Blind Source Separation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Applications of Neural Blind Separation to Signal and Image Processing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 1 - Volume 1
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind separation of multiple binary sources using a single linear mixture
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 05
Blind separation of synchronous co-channel digital signals using anantenna array. I. Algorithms
IEEE Transactions on Signal Processing
Analytical method for blind binary signal separation
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Blind channel identification based on the geometry of the receivedsignal constellation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
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In this paper, we treat the blind separation problem of binary signals and multilevel PAM signals from a single real mixture or a single complex mixture, respectively. Our approach is based on the clustering of the observation values and the close relationship between the position of the cluster centers and the mixing coefficients. Under mild assumptions, our mathematical formulation yields two deterministic algorithms for the blind estimation of the mixing operator. In the real mixture case we derive a finite, recursive algorithm exploiting the arrangement of the centers along the 1-D line, while in the complex mixture case we exploit the properties of the convex hull of the 2-D data cloud to estimate the mixing parameters. In the absence of noise and for any number of sources, both methods yield perfect results. Following the parameter estimation step, the source symbols can be estimated using a nearest neighbor rule. In the noisy case, our error analysis shows that the parameter estimation error increases smoothly with the noise power, while the source estimate bit error rate depends on relative size of the noise power and the minimum distance between the cluster centers.