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Off-the-grid Blind Deconvolution and Demixing
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0003-4519-9204
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0001-6866-6595
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-7926-5081
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). Ericsson Research, Sweden.ORCID iD: 0000-0002-2289-3159
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2023 (English)In: GLOBECOM 2023 - 2023 IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers (IEEE) , 2023, p. 7604-7610Conference paper, Published paper (Refereed)
Abstract [en]

We consider the problem of gridless blind deconvolution and demixing (GB2D) in scenarios where multiple users communicate messages through multiple unknown channels, and a single base station (BS) collects their contributions. This scenario arises in various communication fields, including wireless communications, the Internet of Things, over-the-air computation, and integrated sensing and communications. In this setup, each user's message is convolved with a multi-path channel formed by several scaled and delayed copies of Dirac spikes. The BS receives a linear combination of the convolved signals, and the goal is to recover the unknown amplitudes, continuous-indexed delays, and transmitted waveforms from a compressed vector of measurements at the BS. However, without prior knowledge of the transmitted messages and channels, GB2D is highly challenging and intractable in general. To address this issue, we assume that each user's message follows a distinct modulation scheme living in a known low-dimensional subspace. By exploiting these subspace assumptions and the sparsity of the multipath channels for different users, we transform the nonlinear GB2D problem into a matrix tuple recovery problem from a few linear measurements. To achieve this, we propose a semidefinite programming optimization that exploits the specific low-dimensional structure of the matrix tuple to recover the messages and continuous delays of different communication paths from a single received signal at the BS. Finally, our numerical experiments show that our proposed method effectively recovers all transmitted messages and the continuous delay parameters of the channels with sufficient samples.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. p. 7604-7610
Keywords [en]
Atomic norm minimization, blind channel estimation, blind data recovery, blind deconvolution, blind demixing
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-344558DOI: 10.1109/GLOBECOM54140.2023.10437392ISI: 001178562008030Scopus ID: 2-s2.0-85187336253OAI: oai:DiVA.org:kth-344558DiVA, id: diva2:1845946
Conference
2023 IEEE Global Communications Conference, GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec 4 2023 - Dec 8 2023
Note

Part of ISBN 979-8-3503-1090-0

QC 20240326

Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-04-12Bibliographically approved

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Razavikia, SaeedDaei, SajadSkoglund, MikaelFodor, GaborFischione, Carlo

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