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An Analytical Model for Sparse Network Codes: Field Size Considerations

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An Analytical Model for Sparse Network Codes : Field Size Considerations. / Zarei, Amir; Pahlevani, Peyman; Lucani Rötter, Daniel Enrique.

I: I E E E Communications Letters, Bind 24, Nr. 4, 2020, s. 729 - 733.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avis › Tidsskriftartikel › Forskning › peer review

Harvard

Zarei, A, Pahlevani, P & Lucani Rötter, DE 2020, 'An Analytical Model for Sparse Network Codes: Field Size Considerations', I E E E Communications Letters, bind 24, nr. 4, s. 729 - 733. https://doi.org/10.1109/LCOMM.2020.2965928

APA

Zarei, A., Pahlevani, P., & Lucani Rötter, D. E. (2020). An Analytical Model for Sparse Network Codes: Field Size Considerations. I E E E Communications Letters, 24(4), 729 - 733. https://doi.org/10.1109/LCOMM.2020.2965928

CBE

Zarei A, Pahlevani P, Lucani Rötter DE. 2020. An Analytical Model for Sparse Network Codes: Field Size Considerations. I E E E Communications Letters. 24(4):729 - 733. https://doi.org/10.1109/LCOMM.2020.2965928

MLA

Zarei, Amir, Peyman Pahlevani, og Daniel Enrique Lucani Rötter. "An Analytical Model for Sparse Network Codes: Field Size Considerations". I E E E Communications Letters. 2020, 24(4). 729 - 733. https://doi.org/10.1109/LCOMM.2020.2965928

Vancouver

Zarei A, Pahlevani P, Lucani Rötter DE. An Analytical Model for Sparse Network Codes: Field Size Considerations. I E E E Communications Letters. 2020;24(4):729 - 733. https://doi.org/10.1109/LCOMM.2020.2965928

Author

Zarei, Amir ; Pahlevani, Peyman ; Lucani Rötter, Daniel Enrique. / An Analytical Model for Sparse Network Codes : Field Size Considerations. I: I E E E Communications Letters. 2020 ; Bind 24, Nr. 4. s. 729 - 733.

Bibtex

@article{223a9f0c2f304a6b9cb414b9aa7a1046,
title = "An Analytical Model for Sparse Network Codes: Field Size Considerations",
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.",
author = "Amir Zarei and Peyman Pahlevani and {Lucani R{\"o}tter}, {Daniel Enrique}",
year = "2020",
doi = "10.1109/LCOMM.2020.2965928",
language = "English",
volume = "24",
pages = "729 -- 733",
journal = "I E E E Communications Letters",
issn = "1089-7798",
publisher = "The Institute of Electrical and Electronics Engineers",
number = "4",

}

RIS

TY - JOUR

T1 - An Analytical Model for Sparse Network Codes

T2 - Field Size Considerations

AU - Zarei, Amir

AU - Pahlevani, Peyman

AU - Lucani Rötter, Daniel Enrique

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1109/LCOMM.2020.2965928

DO - 10.1109/LCOMM.2020.2965928

M3 - Journal article

VL - 24

SP - 729

EP - 733

JO - I E E E Communications Letters

JF - I E E E Communications Letters

SN - 1089-7798

IS - 4

ER -