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• W2156636785 abstract "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." @default.
• W2156636785 created "2016-06-24" @default.
• W2156636785 creator A5033473631 @default.
• W2156636785 date "2000-01-01" @default.
• W2156636785 modified "2023-10-13" @default.
• W2156636785 title "Globally convergent blind source separation based on a multiuser kurtosis maximization criterion" @default.
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• W2156636785 doi "https://doi.org/10.1109/78.887044" @default.
• W2156636785 hasPublicationYear "2000" @default.
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