An Analytical Model for Sparse Network Codes: Field Size Considerations

Amir Zarei, Peyman Pahlevani, Daniel Enrique Lucani Rötter

    Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

    Abstract

    One of the by-products of Sparse Network Coding (SNC) is the ability to perform partial decoding, i.e., decoding some original packets prior to collecting all needed coded packets to decode the entire coded data. Due to this ability, SNC has been recently used as a technique for reducing the Average Decoding Delay (ADD) per packet in real-time multimedia applications. This study focuses on characterizing the ADD per packet for SNC considering the impact of finite field size. We present a Markov Chain model that allows us to determine lower bounds on the mean number of transmissions required to decode a fraction of a generation and the ADD per packet of the generation. We validate our model using simulations and show that the smaller finite fields, e.g., q=2^4 , outperform large finite fields, e.g., q=2^{32} , in regard to the ADD per packet and provide a better trade-off between the ADD per packet and the overall number of transmissions to decode a generation.
    Original languageEnglish
    Article number8957070
    JournalI E E E Communications Letters
    Volume24
    Issue4
    Pages (from-to)729 - 733
    Number of pages5
    ISSN1089-7798
    DOIs
    Publication statusPublished - 2020

    Keywords

    • Average decoding delay per packet
    • Random linear network coding
    • Sparse network coding

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    • Scale-loT

      Lucani Rötter, D. E. (Participant)

      01/01/201831/12/2022

      Project: Research

    • Starting Grant

      Lucani Rötter, D. E. (Participant)

      Starting Grant

      01/02/201701/01/2020

      Project: Research

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