The Compress-and-Estimate Coding Scheme for Gaussian Sources
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) co...
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| Veröffentlicht in: | IEEE transactions on wireless communications Jg. 18; H. 9; S. 4344 - 4356 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1536-1276, 1558-2248 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Goldsmith, Andrea J. Rini, Stefano Kipnis, Alon Song, Ruiyang |
| Author_xml | – sequence: 1 givenname: Stefano orcidid: 0000-0003-1681-3316 surname: Rini fullname: Rini, Stefano email: rini.stefano@gmail.com organization: Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan – sequence: 2 givenname: Alon orcidid: 0000-0003-3798-8035 surname: Kipnis fullname: Kipnis, Alon organization: Department of Statistics, Stanford University, Stanford, CA, USA – sequence: 3 givenname: Ruiyang surname: Song fullname: Song, Ruiyang organization: Department of Electrical Engineering, Stanford University, Stanford, CA, USA – sequence: 4 givenname: Andrea J. surname: Goldsmith fullname: Goldsmith, Andrea J. organization: Department of Electrical Engineering, Stanford University, Stanford, CA, USA |
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| SubjectTerms | Central Processing Unit CEO problem Coders Codes Coding Distortion Distortion measurement Electric noise Loss measurement Multiterminal remote source coding mismatched source coding Noise measurement Random noise Remote observing Remote sensors Source coding |
| Title | The Compress-and-Estimate Coding Scheme for Gaussian Sources |
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