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Transversal GRAND for network coded data

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Transversal GRAND for network coded data. / Chatzigeorgiou, Ioannis.
2022 IEEE International Symposium on Information Theory, ISIT 2022. IEEE, 2022. p. 1773-1778 (IEEE International Symposium on Information Theory - Proceedings; Vol. 2022-June).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Chatzigeorgiou, I 2022, Transversal GRAND for network coded data. in 2022 IEEE International Symposium on Information Theory, ISIT 2022. IEEE International Symposium on Information Theory - Proceedings, vol. 2022-June, IEEE, pp. 1773-1778, 2022 IEEE International Symposium on Information Theory, Espoo, Finland, 26/06/22. https://doi.org/10.1109/ISIT50566.2022.9834692

APA

Chatzigeorgiou, I. (2022). Transversal GRAND for network coded data. In 2022 IEEE International Symposium on Information Theory, ISIT 2022 (pp. 1773-1778). (IEEE International Symposium on Information Theory - Proceedings; Vol. 2022-June). IEEE. https://doi.org/10.1109/ISIT50566.2022.9834692

Vancouver

Chatzigeorgiou I. Transversal GRAND for network coded data. In 2022 IEEE International Symposium on Information Theory, ISIT 2022. IEEE. 2022. p. 1773-1778. (IEEE International Symposium on Information Theory - Proceedings). doi: 10.1109/ISIT50566.2022.9834692

Author

Chatzigeorgiou, Ioannis. / Transversal GRAND for network coded data. 2022 IEEE International Symposium on Information Theory, ISIT 2022. IEEE, 2022. pp. 1773-1778 (IEEE International Symposium on Information Theory - Proceedings).

Bibtex

@inproceedings{269c9a06b1fd49bbab00f23e2719bc8c,
title = "Transversal GRAND for network coded data",
abstract = "This paper considers a transmitter, which uses random linear coding (RLC) to encode data packets. The generated coded packets are broadcast to one or more receivers. A receiver can recover the data packets if it gathers a sufficient number of coded packets. We assume that the receiver does not abandon its efforts to recover the data packets if RLC decoding has been unsuccessful; instead, it employs syndrome decoding in an effort to repair erroneously received coded packets before it attempts RLC decoding again. A key assumption of most decoding techniques, including syndrome decoding, is that errors are independently and identically distributed within the received coded packets. Motivated by the `guessing random additive noise decoding' (GRAND) framework, we develop transversal GRAND: an algorithm that exploits statistical dependence in the occurrence of errors, complements RLC decoding and achieves a gain over syndrome decoding, in terms of the probability that the receiver will recover the original data packets.",
author = "Ioannis Chatzigeorgiou",
note = "{\textcopyright}2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; 2022 IEEE International Symposium on Information Theory, ISIT ; Conference date: 26-06-2022 Through 01-07-2022",
year = "2022",
month = aug,
day = "3",
doi = "10.1109/ISIT50566.2022.9834692",
language = "English",
isbn = "9781665421607",
series = "IEEE International Symposium on Information Theory - Proceedings",
publisher = "IEEE",
pages = "1773--1778",
booktitle = "2022 IEEE International Symposium on Information Theory, ISIT 2022",
url = "https://www.isit2022.org/",

}

RIS

TY - GEN

T1 - Transversal GRAND for network coded data

AU - Chatzigeorgiou, Ioannis

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/8/3

Y1 - 2022/8/3

N2 - This paper considers a transmitter, which uses random linear coding (RLC) to encode data packets. The generated coded packets are broadcast to one or more receivers. A receiver can recover the data packets if it gathers a sufficient number of coded packets. We assume that the receiver does not abandon its efforts to recover the data packets if RLC decoding has been unsuccessful; instead, it employs syndrome decoding in an effort to repair erroneously received coded packets before it attempts RLC decoding again. A key assumption of most decoding techniques, including syndrome decoding, is that errors are independently and identically distributed within the received coded packets. Motivated by the `guessing random additive noise decoding' (GRAND) framework, we develop transversal GRAND: an algorithm that exploits statistical dependence in the occurrence of errors, complements RLC decoding and achieves a gain over syndrome decoding, in terms of the probability that the receiver will recover the original data packets.

AB - This paper considers a transmitter, which uses random linear coding (RLC) to encode data packets. The generated coded packets are broadcast to one or more receivers. A receiver can recover the data packets if it gathers a sufficient number of coded packets. We assume that the receiver does not abandon its efforts to recover the data packets if RLC decoding has been unsuccessful; instead, it employs syndrome decoding in an effort to repair erroneously received coded packets before it attempts RLC decoding again. A key assumption of most decoding techniques, including syndrome decoding, is that errors are independently and identically distributed within the received coded packets. Motivated by the `guessing random additive noise decoding' (GRAND) framework, we develop transversal GRAND: an algorithm that exploits statistical dependence in the occurrence of errors, complements RLC decoding and achieves a gain over syndrome decoding, in terms of the probability that the receiver will recover the original data packets.

U2 - 10.1109/ISIT50566.2022.9834692

DO - 10.1109/ISIT50566.2022.9834692

M3 - Conference contribution/Paper

SN - 9781665421607

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 1773

EP - 1778

BT - 2022 IEEE International Symposium on Information Theory, ISIT 2022

PB - IEEE

T2 - 2022 IEEE International Symposium on Information Theory

Y2 - 26 June 2022 through 1 July 2022

ER -