Transversal GRAND for network coded data

Chatzigeorgiou, Ioannis (2022) Transversal GRAND for network coded data. In: 2022 IEEE International Symposium on Information Theory, ISIT 2022 :. IEEE International Symposium on Information Theory - Proceedings . IEEE, FIN, pp. 1773-1778. ISBN 9781665421607

[thumbnail of Camera_Ready_ISIT22]
Text (Camera_Ready_ISIT22)
Camera_Ready_ISIT22.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial.

Download (375kB)


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.

Item Type:
Contribution in Book/Report/Proceedings
Additional Information:
©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.
ID Code:
Deposited By:
Deposited On:
01 Nov 2022 13:35
Last Modified:
09 Jan 2024 00:41