Multi-user pdf estimation based criteria for adaptive blind separation of discrete sources
Signal Processing - Special issue: Information theoretic signal processing
EURASIP Journal on Wireless Communications and Networking
Blind source separation based on constant modulus criterion and signal mutual information
Computers and Electrical Engineering
Blind paraunitary equalization
Signal Processing
Blind paraunitary equalization
Signal Processing
Underdetermined BSS of MISO OSTBC Signals
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Blind source separation based on cumulants with time and frequency non-properties
IEEE Transactions on Audio, Speech, and Language Processing
On the convergence of ICA algorithms with symmetric orthogonalization
IEEE Transactions on Signal Processing
Statistical motion information extraction and representation for semantic video analysis
IEEE Transactions on Circuits and Systems for Video Technology
Steady-state performance of adaptive differential constant modulus multiuser detection
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
IEEE Transactions on Neural Networks
Non orthogonal component analysis: application to anomaly detection
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
| Hi-index | 35.69 |
We consider the problem of recovering blindly (i.e., without the use of training sequences) a number of independent and identically distributed source (user) signals that are transmitted simultaneously through a linear instantaneous mixing channel. The received signals are, hence, corrupted by interuser interference (IUI), and we can model them as the outputs of a linear multiple-input-multiple-output (MIMO) memoryless system. Assuming the transmitted signals to be mutually independent, i.i.d., and to share the same non-Gaussian distribution, a set of necessary and sufficient conditions for the perfect blind recovery (up to scalar phase ambiguities) of all the signals exists and involves the kurtosis as well as the covariance of the output signals. We focus on a straightforward blind constrained criterion stemming from these conditions. From this criterion, we derive an adaptive algorithm for blind source separation, which we call the multiuser kurtosis (MUK) algorithm. At each iteration, the algorithm combines a stochastic gradient update and a Gram-Schmidt orthogonalization procedure in order to satisfy the criterion's whiteness constraints. A performance analysis of its stationary points reveals that the MUK algorithm is free of any stable undesired local stationary points for any number of sources; hence, it is globally convergent to a setting that recovers them all.