Blind demixing and deconvolution with noisy data at near optimal rate

Dominik Stöger; Peter Jung; Felix Krahmer

doi:10.1117/12.2271571

24 August 2017 Blind demixing and deconvolution with noisy data at near optimal rate

Dominik Stöger, Peter Jung, Felix Krahmer

Proceedings Volume 10394, Wavelets and Sparsity XVII; 103941E (2017) https://doi.org/10.1117/12.2271571
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

Blind demixing and deconvolution refers to the problem of simultaneous deconvolution of several source signals from its noisy superposition. This problem appears, amongst others, in the field of Wireless Communication: Many sensors sporadically communicate only short messages over unknown channels. We show that robust recovery of message and channel vectors can be achieved via convex recovery. This requires that random linear encoding is applied at the devices and that the number of required measurements at the receiver scales essentially with the degrees of freedom of the overall estimation problem. Thus, the scaling is linear in the number of source signals. This significantly improves previous results.

Conference Presentation

Citation Download Citation

Dominik Stöger, Peter Jung, and Felix Krahmer "Blind demixing and deconvolution with noisy data at near optimal rate", Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941E (24 August 2017); https://doi.org/10.1117/12.2271571

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