Matlab codes to implement the DeepVQCS method proposed in "Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks" (M. Leinonen and M. Codreanu)

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Matlab codes to implement the DeepVQCS method proposed in "Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks" (M. Leinonen and M. Codreanu)

Description

Matlab codes to realize the DeepVQCS architecture and its training proposed in the journal paper by M. Leinonen and M. Codreanu, "Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks", IEEE Open Journal of the Communications Society, Vol. 1, pp. 1278 - 1294, Aug. 2020. DOI: 10.1109/OJCOMS.2020.3020131
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Year of publication

2021

Authors

University of Oulu

CWC - Radioteknologiat - Publisher

Markus Leinonen Orcid -palvelun logo - Creator

Other information

Fields of science

Electronic, automation and communications engineering, electronics

Language

English

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

Signal Processing, machine learning, Compressed sensing, data compression, deep neural network, supervised learning, vector quantization

Subject headings

wireless communication
Matlab codes to implement the DeepVQCS method proposed in "Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks" (M. Leinonen and M. Codreanu) - Research.fi