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Proceedings/Recueil Des Communications CAMSAP 2019 - Proceedings Année : 2019

Blind Calibration for Sparse Regression: A State Evolution Analysis

Résumé

Sparse regression, such as the one used in compressed sensing, allows to acquire compressible signals with a small number of measurements. As such, a correct calibration of a potential hardware problem is a central issue. Blind calibration, that is performing at the same time calibration and compressed sensing when the training signals are sparse but unknown, is thus particularly appealing. A potential approach was suggested by Schülke et al, using an approximate message passing (AMP) for blind calibration (cal-AMP). Here, we show that the asymptotic performances of this algorithm can be analysed by an exact state evolution equation. It allows to confirm that cal-AMP requires a smaller number of measurements and/or signals in order to perform with respect to standard convex approaches, and opens the way to more complex message passing techniques. © 2019 IEEE.
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Dates et versions

cea-04542590 , version 1 (11-04-2024)

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Marylou Gabrie, Jean Barbier, Florent Krzakala, Lenka Zdeborova. Blind Calibration for Sparse Regression: A State Evolution Analysis. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), CAMSAP 2019 - Proceedings, 9022479, IEEE, pp.649-653, 2019, 978-172815549-4. ⟨10.1109/CAMSAP45676.2019.9022479⟩. ⟨cea-04542590⟩
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