| Paper: | MLSP-P6.12 | ||
| Session: | Learning Theory and Models | ||
| Time: | Thursday, May 20, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Blind Signal Separation and Independent Component Analysis | ||
| Title: | BLIND SOURCE SEPARATION WITH RANDOMIZED GRAM-SHMIDT ORTHOGONALIZATION FOR SHORT BURST SYSTEMS | ||
| Authors: | Constantinos Papadias; Lucent Technologies | ||
| Alexandr Kuzminskiy; Lucent Technologies | |||
| Abstract: | A blind source separation problem for short burst systems is addressed by means of a constant modulus technique under orthogonal constraints. It is shown that a conventional Gram-Shmidt orthogonalization procedure normally exploited in similar applications may cause a non-uniform misadjustment distribution among the receiver outputs leading to an overal performance degradation. We propose a modified algorithm based on random reordering of the weight vectors before the orthogonalization stage and demonstrate its efficiency by means of simulations in a short burst MIMO environment. | ||
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