<div class="csl-bib-body">
<div class="csl-entry">Birgmeier, S. C., & Görtz, N. (2019). Exploiting General Multi-Dimensional Priors in Compressed-Sensing Reconstruction. In <i>Proceedings International ITG Conference on Systems, Communications and Coding (SCC 2019)</i> (pp. 113–118). VDE-Verlag. https://doi.org/10.30420/454862020</div>
</div>
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dc.identifier.isbn
9783800748624
-
dc.identifier.uri
http://hdl.handle.net/20.500.12708/76771
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dc.description.abstract
Message passing based algorithms have been shown to perform well in terms of minimum mean-squared error for high-dimensional signals composed of independent and identically distributed one-dimensional and sparse components. These conditions limit the applicability and performance of these algorithms since dependencies among components are not used during recovery. A detailed derivation is given that, as a novelty, extends the known derivation of the conventional Bayesian
Approximate Message Passing scheme (BAMP) to general multi-dimensional priors. The proposed algorithms significantly reduce the number of samples required for reconstruction compared to methods which do not exploit dependencies. Applications include multiple-measurement vector (MMV) problems, group sparsity as well as symbol recovery in MIMO systems and reconstruction in the case of general, non-sparse dependencies between components.
https://ieeexplore.ieee.org/document/8661317
de
dc.description.abstract
Message passing based algorithms have been shown to perform well in terms of minimum mean-squared error for high-dimensional signals composed of independent and identically distributed one-dimensional and sparse components. These conditions limit the applicability and performance of these algorithms since dependencies among components are not used during recovery. A detailed derivation is given that, as a novelty, extends the known derivation of the conventional Bayesian
Approximate Message Passing scheme (BAMP) to general multi-dimensional priors. The proposed algorithms significantly reduce the number of samples required for reconstruction compared to methods which do not exploit dependencies. Applications include multiple-measurement vector (MMV) problems, group sparsity as well as symbol recovery in MIMO systems and reconstruction in the case of general, non-sparse dependencies between components.
https://ieeexplore.ieee.org/document/8661317
en
dc.language.iso
en
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dc.publisher
VDE-Verlag
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dc.subject
Signal Processing / Compressed Sensing / Approximate Message Passing
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dc.title
Exploiting General Multi-Dimensional Priors in Compressed-Sensing Reconstruction
en
dc.type
Konferenzbeitrag
de
dc.type
Inproceedings
en
dc.relation.publication
Proceedings International ITG Conference on Systems, Communications and Coding (SCC 2019)
-
dc.relation.isbn
978-3-8007-4862-4
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dc.description.startpage
113
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dc.description.endpage
118
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings International ITG Conference on Systems, Communications and Coding (SCC 2019)
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tuw.peerreviewed
true
-
tuw.relation.publisher
VDE
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tuw.researchTopic.id
I7
-
tuw.researchTopic.name
Telecommunication
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tuw.researchTopic.value
100
-
tuw.publication.orgunit
E389-03 - Forschungsbereich Signal Processing
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tuw.publisher.doi
10.30420/454862020
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dc.description.numberOfPages
6
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tuw.event.name
International ITG Conference on Systems, Communications and Coding (SCC 2019)
