TY - JOUR
T1 - The Compress-And-Estimate Coding Scheme for Gaussian Sources
AU - Rini, Stefano
AU - Kipnis, Alon
AU - Song, Ruiyang
AU - Goldsmith, Andrea J.
N1 - Funding Information:
Manuscript received August 21, 2018; revised March 10, 2019 and April 3, 2019; accepted May 30, 2019. Date of publication June 24, 2019; date of current version September 10, 2019. This work was supported in part by the Ministry of Science and Technology (MOST) under Grant 106-2221-E-009-044, in part by the NSF under Grant CCF-1320628, in part by the NSF Center for Science of Information (CSoI) under Grant CCF-0939370, and in part by the U.S.–Israel Binational Science Foundation (BSF) Transformative Science under Grant 2010505. This paper was presented in part at the Information Theory Worshop (ITW) 2016 [1] and 2017 [2]. (Corresponding author: Stefano Rini.) S. Rini is with the Department of Electrical Engineering, National Chiao Tung University, Hsinchu 30010, Taiwan (e-mail: rini.stefano@gmail.com).
Funding Information:
This work was supported in part by the Ministry of Science and Technology (MOST) under Grant 106-2221-E-009-044, in part by the NSF under Grant CCF-1320628, in part by the NSF Center for Science of Information (CSoI) under Grant CCF-0939370, and in part by the U.S.-Israel Binational Science Foundation (BSF) Transformative Science under Grant 2010505.
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - We consider the multiterminal remote source coding problem of estimating a Gaussian signal from a bit-restricted representation of distributed linear measurements corrupted by additive white Gaussian noise. For this problem, we study the performance of the multiterminal compress-And-estimate (CE) coding scheme in which multiple remote encoders compress their measurements so as to minimize a local distortion measure which depends solely on the distribution of these measurements. In reconstruction, the decoder estimates the signal from the lossy-compressed measurements having full knowledge of the statistics of the source signal and the noisy measurements. The CE coding scheme is motivated by the scenario in which source encoders, due to their limited capabilities, operate according to a pre-determined compression strategy and cannot adapt to the sensing environment while the fusion center has full knowledge and computational capabilities. We focus, in particular, on two scenarios: The centralized observation model in which measurements are collected at a single remote encoder and the distributed observation model where measurements are provided to multiple remote sensors. In both scenarios, we investigate the performance attainable through the CE coding scheme in which the measurements are compressed according to a quadratic distortion measure and compare it to the performance of the coding scheme having full system knowledge.
AB - We consider the multiterminal remote source coding problem of estimating a Gaussian signal from a bit-restricted representation of distributed linear measurements corrupted by additive white Gaussian noise. For this problem, we study the performance of the multiterminal compress-And-estimate (CE) coding scheme in which multiple remote encoders compress their measurements so as to minimize a local distortion measure which depends solely on the distribution of these measurements. In reconstruction, the decoder estimates the signal from the lossy-compressed measurements having full knowledge of the statistics of the source signal and the noisy measurements. The CE coding scheme is motivated by the scenario in which source encoders, due to their limited capabilities, operate according to a pre-determined compression strategy and cannot adapt to the sensing environment while the fusion center has full knowledge and computational capabilities. We focus, in particular, on two scenarios: The centralized observation model in which measurements are collected at a single remote encoder and the distributed observation model where measurements are provided to multiple remote sensors. In both scenarios, we investigate the performance attainable through the CE coding scheme in which the measurements are compressed according to a quadratic distortion measure and compare it to the performance of the coding scheme having full system knowledge.
KW - CEO problem
KW - Multiterminal remote source coding mismatched source coding
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U2 - 10.1109/TWC.2019.2923399
DO - 10.1109/TWC.2019.2923399
M3 - Article
AN - SCOPUS:85072208553
SN - 1536-1276
VL - 18
SP - 4344
EP - 4356
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 9
M1 - 8744500
ER -