On the Gap between Scalar and Vector Solutions of Generalized Combination Networks

Hedongliang Liu; Hengjia Wei; Sven Puchinger; Antonia Wachter-Zeh; Moshe Schwartz

doi:10.1109/ISIT44484.2020.9173942

On the Gap between Scalar and Vector Solutions of Generalized Combination Networks

Hedongliang Liu, Hengjia Wei, Sven Puchinger, Antonia Wachter-Zeh, Moshe Schwartz

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

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Abstract

We study scalar-linear and vector-linear solutions to the generalized combination network. We derive new upper and lower bounds on the maximum number of nodes in the middle layer, depending on the network parameters. These bounds improve and extend the parameter range of known bounds. Using these new bounds we present a general lower bound on the gap in the alphabet size between scalar-linear and vector-linear solutions.

Original language	English
Title of host publication	Proceedings of 2020 IEEE International Symposium on Information Theory
Publisher	IEEE
Publication date	Jun 2020
Pages	1646-1651
Article number	9173942
ISBN (Electronic)	9781728164328
DOIs	https://doi.org/10.1109/ISIT44484.2020.9173942
Publication status	Published - Jun 2020
Event	2020 IEEE International Symposium on Information Theory - Virtual event, Los Angeles, United States Duration: 21 Jun 2020 → 26 Jun 2020 https://2020.ieee-isit.org/

Conference

Conference	2020 IEEE International Symposium on Information Theory
Location	Virtual event
Country/Territory	United States
City	Los Angeles
Period	21/06/2020 → 26/06/2020
Internet address	https://2020.ieee-isit.org/

Series	IEEE International Symposium on Information Theory - Proceedings
Volume	2020-June
ISSN	2157-8095

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Liu, H., Wei, H., Puchinger, S., Wachter-Zeh, A., & Schwartz, M. (2020). On the Gap between Scalar and Vector Solutions of Generalized Combination Networks. In Proceedings of 2020 IEEE International Symposium on Information Theory (pp. 1646-1651). [9173942] IEEE. IEEE International Symposium on Information Theory - Proceedings Vol. 2020-June https://doi.org/10.1109/ISIT44484.2020.9173942

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author = "Hedongliang Liu and Hengjia Wei and Sven Puchinger and Antonia Wachter-Zeh and Moshe Schwartz",

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Liu, H, Wei, H, Puchinger, S, Wachter-Zeh, A & Schwartz, M 2020, On the Gap between Scalar and Vector Solutions of Generalized Combination Networks. in Proceedings of 2020 IEEE International Symposium on Information Theory., 9173942, IEEE, IEEE International Symposium on Information Theory - Proceedings, vol. 2020-June, pp. 1646-1651, 2020 IEEE International Symposium on Information Theory, Los Angeles, California, United States, 21/06/2020. https://doi.org/10.1109/ISIT44484.2020.9173942

On the Gap between Scalar and Vector Solutions of Generalized Combination Networks. / Liu, Hedongliang; Wei, Hengjia; Puchinger, Sven; Wachter-Zeh, Antonia; Schwartz, Moshe.

Proceedings of 2020 IEEE International Symposium on Information Theory. IEEE, 2020. p. 1646-1651 9173942 (IEEE International Symposium on Information Theory - Proceedings, Vol. 2020-June).

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

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AB - We study scalar-linear and vector-linear solutions to the generalized combination network. We derive new upper and lower bounds on the maximum number of nodes in the middle layer, depending on the network parameters. These bounds improve and extend the parameter range of known bounds. Using these new bounds we present a general lower bound on the gap in the alphabet size between scalar-linear and vector-linear solutions.

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Liu H, Wei H, Puchinger S, Wachter-Zeh A, Schwartz M. On the Gap between Scalar and Vector Solutions of Generalized Combination Networks. In Proceedings of 2020 IEEE International Symposium on Information Theory. IEEE. 2020. p. 1646-1651. 9173942. (IEEE International Symposium on Information Theory - Proceedings, Vol. 2020-June). https://doi.org/10.1109/ISIT44484.2020.9173942

On the Gap between Scalar and Vector Solutions of Generalized Combination Networks

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