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Blind demixing and deconvolution with noisy data at near optimal rate
Author(s): Dominik Stöger; Peter Jung; Felix Krahmer
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Paper 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.

Paper Details

Date Published: 24 August 2017
PDF: 8 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 103941E (24 August 2017); doi: 10.1117/12.2271571
Show Author Affiliations
Dominik Stöger, Technische Univ. München (Germany)
Peter Jung, Technische Univ. Berlin (Germany)
Felix Krahmer, Technische Univ. München (Germany)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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