Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC

Shraga I. Bross; Yaron Laufer

doi:10.1109/isit.2015.7282497

Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We consider the transmission of a bivariate Gaussian memoryless source over a two-user additive Gaussian multiple-access channel with unidirectional conferencing. Here, prior to each transmission block Encoder 1 is allowed to communicate with Encoder 2 via a unidirectional noise-free bit-pipe of given capacity. We derive a necessary condition for the achievability of a distortion pair expressed as a function of the channel SNR and of the source correlation. In the high-SNR regime, and when the capacity of the conference channel is unlimited, the necessary condition is shown to agree with a sufficient condition derived previously by the authors. We evaluate the precise high-SNR asymptotics for a subset of distortion pairs when the conferencing capacity is unlimited in which case we show that the problem is separable.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	461-465
Number of pages	5
ISBN (Electronic)	9781467377041
DOIs	https://doi.org/10.1109/isit.2015.7282497
State	Published - 28 Sep 2015
Event	IEEE International Symposium on Information Theory, ISIT 2015 - Hong Kong, Hong Kong Duration: 14 Jun 2015 → 19 Jun 2015

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
Volume	2015-June
ISSN (Print)	2157-8095

Conference

Conference	IEEE International Symposium on Information Theory, ISIT 2015
Country/Territory	Hong Kong
City	Hong Kong
Period	14/06/15 → 19/06/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Funding

The work of S. Bross was supported by the Israel Science Foundation under Grant 497/09 and Grant 455/14.

Funders	Funder number
Israel Science Foundation	455/14, 497/09

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10.1109/isit.2015.7282497

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Bross, S. I., & Laufer, Y. (2015). Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC. In Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015 (pp. 461-465). Article 7282497 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2015-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isit.2015.7282497

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Bross, SI & Laufer, Y 2015, Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC. in Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015., 7282497, IEEE International Symposium on Information Theory - Proceedings, vol. 2015-June, Institute of Electrical and Electronics Engineers Inc., pp. 461-465, IEEE International Symposium on Information Theory, ISIT 2015, Hong Kong, Hong Kong, 14/06/15. https://doi.org/10.1109/isit.2015.7282497

Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC. / Bross, Shraga I.; Laufer, Yaron.
Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 461-465 7282497 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2015-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Bross SI, Laufer Y. Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC. In Proceedings - 2015 IEEE International Symposium on Information Theory, ISIT 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 461-465. 7282497. (IEEE International Symposium on Information Theory - Proceedings). doi: 10.1109/isit.2015.7282497

Necessary condition for sending a bivariate Gaussian source over the 'asymmetric' Gaussian MAC

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