Blind Demixing and Deconvolution with Noisy Data: Near-optimal Rate

Abstract

We consider simultaneous blind deconvolution of r source signals from its 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. is applied 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 this significantly improves over recent results.