Blind Demixing and Deconvolution at Near-Optimal Rate

2017/04/13 by Peter Jung, Jung, Peter, Felix Krahmer +3 · 5 citations
Computer Science · Mathematics · #FOS: Computer and information sciences #Information Theory (cs.IT) #cs.IT #math.IT

paper · pdf · doi:10.48550/arxiv.1704.04178

49 pages, 1 figure; v2: a few typos removed

arxiv created 2017/05/02 · arxiv updated 2017/05/04

Abstract

We consider simultaneous blind deconvolution of r source signals from their noisy superposition, a problem also referred to blind demixing and deconvolution. This signal processing problem occurs in the context of the Internet of Things where a massive number of sensors sporadically communicate only short messages over unknown channels. We show that robust recovery of message and channel vectors can be achieved via convex optimization when random linear encoding using i.i.d. complex Gaussian matrices is used at the devices and the number of required measurements at the receiver scales with the degrees of freedom of the overall estimation problem. Since the scaling is linear in r our result significantly improves over recent works.

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