Decentralized expected consistent signal recovery for quantization measurements

Chang Jen Wang, Chao Kai Wen, Shang-Ho Tsai, Shi Jin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Signal recovery through coarse quantization of a linear transform output has many applications in engineering, such as channel estimation and signal detection in massive MIMO systems. A recently proposed scheme, known as generalized expectation consistent signal recovery (GEC-SR), can achieve Bayesian inference and exhibit better robustness than many existing methods. However, recovering signals with large transform matrices continue to present a computational burden for GEC-SR. In this study, we develop a novel decentralized architecture by leveraging the core framework of GEC-SR called “deGEC-SR.” deGEC-SR offers excellent performance as GEC-SR and runs tens of times faster than GEC-SR. We derive the theoretical state evolution of deGEC-SR and demonstrate its accuracy using numerical results.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5620-5624
Number of pages5
ISBN (Electronic)9781509066315
ISBN (Print)978-1-7281-5090-1
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Bayes-optimal inference
  • Decentralized algorithm
  • Expectation consistent
  • Quantization measurement

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