Practical compression methods for quantized compressed sensing

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.

Leinonen Markus, Codreanu Marian, Juntti Markku

A4 Article in conference proceedings


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