Adaptive processing of blind source separation through `ICA with OS´

Blind source separation problem whose solution is vital in numerous applications in communications. We are proposing a multistage procedure to separate N original sources from N instantaneous mixtures. The goal is to extract the parameters of the unknown mixture in a deflation approach. In each stage of the procedure a novel cost function is applied. The cost function is derived from the properties of the cdf (cumulative density function) to perform an appropriate independent measure by means of order statistics (OS) (unbiased estimator of the cdf). The key-point of this contribution is the adaptive algorithm applied to optimize our cost function using gradient descent techniques