M. Leinonen, M. Codreanu and M. Juntti, "Practical Compression Methods for Quantized Compressed Sensing," IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France, 2019, pp. 756-761. doi: 10.1109/INFCOMW.2019.8845224
Practical compression methods for quantized compressed sensing
|Author:||Leinonen, Markus1; Codreanu, Marian2; Juntti, Markku1|
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, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2019100431190
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2019-10-04
In order to save energy of low-power sensors in Internet of Things applications, minimizing the number of bits to compress and communicate real-valued sources with a predefined distortion becomes crucial. In such a lossy source coding context, we study rate-distortion (RD) performance of various single-sensor quantized compressed sensing (QCS) schemes for compressing sparse signals via quantized/encoded noisy linear measurements. The paper combines and refines the recent advances of QCS algorithm designs and theoretical analysis. In particular, several practical symbol-by-symbol quantizer based QCS methods of different complexities relying on 1) compress-and-estimate, 2) estimate-and-compress, and 3) support-estimation-and-compress strategies are proposed. Simulation results demonstrate the RD performances of different schemes and compare them to the information-theoretic limits.
|Pages:||756 - 761|
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)
IEEE INFOCOM Conference on Computer Communications Workshops
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
The work of M. Leinonen and M. Codreanu has been financially supported by Infotech Oulu. M. Codreanu would like to acknowledge the support of the European Union's Horizon 2020 research and innovation programme under the Marie Skodowska-Curie Grant Agreement No. 793402 (COMPRESS NETS). The research has also been financially supported by the Academy of Finland, and Academy of Finland 6Genesis Flagship (grant 318927).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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