Quantized compressed sensing via deep neural networks
|Author:||Leinonen, Markus1; Codreanu, Marian2|
1Centre for Wireless Communications – Radio Technologies FI-90014, University of Oulu, Finland
2Department of Science and Technology Linköping University, Sweden
|Online Access:||PDF Full Text (PDF, 0.9 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020050725529
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2020-05-07
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.
|Pages:||1 - 5|
2020 2nd 6G Wireless Summit (6G SUMMIT)
6G Wireless Summit
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
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