Decoding Algorithms for Random Linear Network Codes

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.
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Communication dans un congrès
Vicente Casares-Giner; Pietro Manzoni; Ana Pont. International IFIP TC 6 Workshops PE-CRN, NC-Pro, WCNS, and SUNSET 2011 Held at NETWORKING 2011 (NETWORKING), May 2011, Valencia, Spain. Springer, Lecture Notes in Computer Science, LNCS-6827, pp.129-136, 2011, NETWORKING 2011 Workshops. 〈10.1007/978-3-642-23041-7_13〉
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Janus Heide, Morten Pedersen, Frank Fitzek. Decoding Algorithms for Random Linear Network Codes. Vicente Casares-Giner; Pietro Manzoni; Ana Pont. International IFIP TC 6 Workshops PE-CRN, NC-Pro, WCNS, and SUNSET 2011 Held at NETWORKING 2011 (NETWORKING), May 2011, Valencia, Spain. Springer, Lecture Notes in Computer Science, LNCS-6827, pp.129-136, 2011, NETWORKING 2011 Workshops. 〈10.1007/978-3-642-23041-7_13〉. 〈hal-01587835〉

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