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Transversal GRAND for network coded data

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

Published
Publication date3/08/2022
Host publication2022 IEEE International Symposium on Information Theory, ISIT 2022
PublisherIEEE
Pages1773-1778
Number of pages6
ISBN (Electronic)9781665421591
ISBN (Print)9781665421607
<mark>Original language</mark>English
Event2022 IEEE International Symposium on Information Theory - Aalto University, Espoo, Finland
Duration: 26/06/20221/07/2022
https://www.isit2022.org/

Conference

Conference2022 IEEE International Symposium on Information Theory
Abbreviated titleISIT
Country/TerritoryFinland
CityEspoo
Period26/06/221/07/22
Internet address

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2022-June
ISSN (Print)2157-8095

Conference

Conference2022 IEEE International Symposium on Information Theory
Abbreviated titleISIT
Country/TerritoryFinland
CityEspoo
Period26/06/221/07/22
Internet address

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.

Bibliographic note

©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.