Upscaling Vector Approximate Message Passing

Nikolai Skuratovs, Michael Davies

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we consider the problem of recovering a signal x of size N from noisy and compressed measurements y = Ax + w of size M, where the measurement matrix A is right-orthogonally invariant (ROI). Vector Approximate Message Passing (VAMP) demonstrates great reconstruction results for even highly ill-conditioned matrices A in relatively few iterations. However, performing each iteration is challenging due to either computational or memory point of view. On the other hand, a recently proposed Conjugate Gradient (CG) Expectation Propagation (CG-EP) framework is able to sacrifice some performance for efficiency, but requires access to exact singular spectrum of A. In this work we develop a CG-VAMP algorithm that does not require such information, is feasible to implement and converges to the neighborhood of the original VAMP.
Original languageEnglish
Title of host publicationICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE Xplore
Volume2020
ISBN (Electronic)978-1-5090-6631-5
ISBN (Print)978-1-5090-6632-2
DOIs
Publication statusPublished - 14 May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing - Barcelona, Spain
Duration: 4 May 20208 May 2020
Conference number: 45

Publication series

NameInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE Xplore
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2020
CountrySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Compressed Sensing
  • Vector Approximate Message Passing
  • Expectation Propagation

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