University of Oulu

M. Leinonen and M. Codreanu, "Quantized Compressed Sensing via Deep Neural Networks," 2020 2nd 6G Wireless Summit (6G SUMMIT), Levi, Finland, 2020, pp. 1-5, doi: 10.1109/6GSUMMIT49458.2020.9083783

Quantized compressed sensing via deep neural networks

Saved in:
Author: Leinonen, Markus1; Codreanu, Marian2
Organizations: 1Centre for Wireless Communications – Radio Technologies FI-90014, University of Oulu, Finland
2Department of Science and Technology Linköping University, Sweden
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020050725529
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-05-07
Description:

Abstract

Compressed sensing (CS) is an efficient technique to acquire sparse signals in many wireless applications to, e.g., reduce the amount of data and save low-power sensors’ batteries. This paper addresses efficient acquisition of sparse sources through quantized noisy compressive measurements where the encoder and decoder are realized by deep neural networks (DNNs). We devise a DNN based quantized compressed sensing (QCS) method aiming at minimizing the mean-square error of the signal reconstruction. Once trained offline, the proposed method enjoys extremely fast and low complexity decoding in the online communication phase. Simulation results demonstrate the superior rate-distortion performance of the proposed method compared to a polynomial-complexity QCS reconstruction scheme.

see all

ISBN: 978-1-7281-6047-4
ISBN Print: 978-1-7281-6048-1
Pages: 1 - 5
DOI: 10.1109/6GSUMMIT49458.2020.9083783
OADOI: https://oadoi.org/10.1109/6GSUMMIT49458.2020.9083783
Host publication: 2020 2nd 6G Wireless Summit (6G SUMMIT)
Conference: 6G Wireless Summit
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: The work has been financially supported in part by Infotech Oulu, the Academy of Finland (grant 323698), and Academy of Finland 6Genesis Flagship (grant 318927). The work of M. Leinonen has also been financially supported in part by the Academy of Finland (grant 319485). M. Codreanu would like to acknowledge the support of the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 793402 (COMPRESS NETS).
Academy of Finland Grant Number: 323698
318927
319485
Detailed Information: 323698 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.