Decoding Algorithms for Random Linear Network Codes

Janus Heide, Morten Videbæk Pedersen, Frank Fitzek

Research output: Contribution to journalConference article in JournalResearchpeer-review

9 Citations (Scopus)
754 Downloads (Pure)

Abstract

We consider the problem of efficient decoding of a random linear code over a finite field. In particular we are interested in the case where the code is random, relatively sparse, and use the binary finite field as an example. The goal is to decode the data using fewer operations to potentially achieve a high coding throughput, and reduce energy consumption.We use an on-the-fly version of the Gauss-Jordan algorithm as a baseline, and provide several simple improvements to reduce the number of operations needed to perform decoding. Our tests show that the improvements can reduce the number of operations used during decoding with 10-20% on average depending on the code parameters.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Volume6827
Pages (from-to)129-137
Number of pages8
ISSN0302-9743
DOIs
Publication statusPublished - 13 May 2011
EventNetworking 2011, NC-Pro - Valencia, Spain
Duration: 13 May 201113 May 2011

Workshop

WorkshopNetworking 2011, NC-Pro
CountrySpain
CityValencia
Period13/05/201113/05/2011

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Linear networks
Decoding
Galois field
Decode
Linear Codes
Gauss
Energy Consumption
Baseline
Throughput
Energy utilization
Coding
Binary

Cite this

Heide, Janus ; Pedersen, Morten Videbæk ; Fitzek, Frank. / Decoding Algorithms for Random Linear Network Codes. In: Lecture Notes in Computer Science. 2011 ; Vol. 6827. pp. 129-137.
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title = "Decoding Algorithms for Random Linear Network Codes",
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Decoding Algorithms for Random Linear Network Codes. / Heide, Janus; Pedersen, Morten Videbæk; Fitzek, Frank.

In: Lecture Notes in Computer Science, Vol. 6827, 13.05.2011, p. 129-137.

Research output: Contribution to journalConference article in JournalResearchpeer-review

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AU - Fitzek, Frank

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AB - We consider the problem of efficient decoding of a random linear code over a finite field. In particular we are interested in the case where the code is random, relatively sparse, and use the binary finite field as an example. The goal is to decode the data using fewer operations to potentially achieve a high coding throughput, and reduce energy consumption.We use an on-the-fly version of the Gauss-Jordan algorithm as a baseline, and provide several simple improvements to reduce the number of operations needed to perform decoding. Our tests show that the improvements can reduce the number of operations used during decoding with 10-20% on average depending on the code parameters.

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