ALPHABET-BASED DEFLATION FOR BLIND SOURCE EXTRACTION IN UNDERDETERMINED MIXTURES
Vicente Zarzoso, Pierre Comon · 2006
The deflation approach to blind source extraction esti-mates the source signals one by one. The contribution of the latest source estimate is computed via linear regression and subtracted from the observations before performing a new extraction. In the context of digital communications, novel alphabet-based contrast criteria can naturally be de-fined, leading to the recently proposed parallel deflation concept. We analyse the use of such criteria in the chal-lenging scenario of underdetermined mixtures, where the sources outnumber the sensors. Due to the limitations of linear extraction, projection on the signal alphabet before the regression-subtraction stage is shown to be capital for a successful source estimation. It is also demonstrated that alphabet-based criteria outperform the constant modulus (CM) principle, even for CM-type sources. More interest-ingly, classical deflation can improve on parallel deflation, but requires a refinement to render its performance robust to the extraction ordering.