Guessing Random Additive Noise Decoding of Network Coded Data Transmitted Over Burst Error Channels

We consider a transmitter that encodes data packets using network coding and broadcasts coded packets. A receiver employing network decoding recovers the data packets if a sufficient number of error-free coded packets are gathered. The receiver does not abandon its efforts to recover the data packet...

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Vydané v:	IEEE transactions on vehicular technology Ročník 73; číslo 9; s. 12842 - 12857
Hlavní autori:	Chatzigeorgiou, Ioannis, Savostyanov, Dmitry
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.09.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Additive noise Algorithms Burst noise Decoding Encoding Error reduction guessing random additive noise decoding (GRAND) Iterative decoding Linear codes Maximum likelihood decoding network coding network decoding Packets (communication) random linear coding Receivers Statistical analysis syndrome decoding Vectors
ISSN:	0018-9545, 1939-9359
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Abstract	We consider a transmitter that encodes data packets using network coding and broadcasts coded packets. A receiver employing network decoding recovers the data packets if a sufficient number of error-free coded packets are gathered. The receiver does not abandon its efforts to recover the data packets if network decoding is unsuccessful; instead, it employs syndrome decoding (SD) in an effort to repair erroneous received coded packets, and then reattempts network decoding. Most decoding techniques, including SD, assume that errors are independently and identically distributed within received coded packets. Motivated by the guessing random additive noise decoding (GRAND) framework, we propose transversal GRAND (T-GRAND): an algorithm that exploits statistical dependence in the occurrence of errors, complements network decoding and recovers all data packets with a higher probability than SD. T-GRAND examines error vectors in order of their likelihood of occurring and altering the transmitted packets. Calculation and sorting of the likelihood values of all error vectors is a simple but computationally expensive process. To reduce the complexity of T-GRAND, we take advantage of the properties of the likelihood function and develop an efficient method, which identifies the most likely error vectors without computing and ordering all likelihood values.
AbstractList	We consider a transmitter that encodes data packets using network coding and broadcasts coded packets. A receiver employing network decoding recovers the data packets if a sufficient number of error-free coded packets are gathered. The receiver does not abandon its efforts to recover the data packets if network decoding is unsuccessful; instead, it employs syndrome decoding (SD) in an effort to repair erroneous received coded packets, and then reattempts network decoding. Most decoding techniques, including SD, assume that errors are independently and identically distributed within received coded packets. Motivated by the guessing random additive noise decoding (GRAND) framework, we propose transversal GRAND (T-GRAND): an algorithm that exploits statistical dependence in the occurrence of errors, complements network decoding and recovers all data packets with a higher probability than SD. T-GRAND examines error vectors in order of their likelihood of occurring and altering the transmitted packets. Calculation and sorting of the likelihood values of all error vectors is a simple but computationally expensive process. To reduce the complexity of T-GRAND, we take advantage of the properties of the likelihood function and develop an efficient method, which identifies the most likely error vectors without computing and ordering all likelihood values.
Author	Chatzigeorgiou, Ioannis Savostyanov, Dmitry
Author_xml	– sequence: 1 givenname: Ioannis orcidid: 0000-0001-6802-3434 surname: Chatzigeorgiou fullname: Chatzigeorgiou, Ioannis email: i.chatzigeorgiou@lancaster.ac.uk organization: School of Computing and Communications, Lancaster University, Lancaster, U.K – sequence: 2 givenname: Dmitry orcidid: 0000-0001-9994-1342 surname: Savostyanov fullname: Savostyanov, Dmitry email: d.savostyanov@essex.ac.uk organization: School of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester, U.K
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SubjectTerms	Additive noise Algorithms Burst noise Decoding Encoding Error reduction guessing random additive noise decoding (GRAND) Iterative decoding Linear codes Maximum likelihood decoding network coding network decoding Packets (communication) random linear coding Receivers Statistical analysis syndrome decoding Vectors
Title	Guessing Random Additive Noise Decoding of Network Coded Data Transmitted Over Burst Error Channels
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