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

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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.