Skip to content

Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

Research output: Contribution to journalArticle

Standard

Reliability of Broadcast Communications Under Sparse Random Linear Network Coding. / Brown, Suzie; Johnson, Oliver; Tassi, Andrea.

In: IEEE Transactions on Vehicular Technology, Vol. 67, No. 5, 12.05.2018, p. 4677-4682.

Research output: Contribution to journalArticle

Harvard

Brown, S, Johnson, O & Tassi, A 2018, 'Reliability of Broadcast Communications Under Sparse Random Linear Network Coding', IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 4677-4682. https://doi.org/10.1109/TVT.2018.2790436

APA

Brown, S., Johnson, O., & Tassi, A. (2018). Reliability of Broadcast Communications Under Sparse Random Linear Network Coding. IEEE Transactions on Vehicular Technology, 67(5), 4677-4682. https://doi.org/10.1109/TVT.2018.2790436

Vancouver

Brown S, Johnson O, Tassi A. Reliability of Broadcast Communications Under Sparse Random Linear Network Coding. IEEE Transactions on Vehicular Technology. 2018 May 12;67(5):4677-4682. https://doi.org/10.1109/TVT.2018.2790436

Author

Brown, Suzie ; Johnson, Oliver ; Tassi, Andrea. / Reliability of Broadcast Communications Under Sparse Random Linear Network Coding. In: IEEE Transactions on Vehicular Technology. 2018 ; Vol. 67, No. 5. pp. 4677-4682.

Bibtex

@article{4f1547147d1d4d089a69c6adef3f666d,
title = "Reliability of Broadcast Communications Under Sparse Random Linear Network Coding",
abstract = "Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kind of coding technique. In particular, by exploiting the Stein--Chen method, we provide a novel and general performance framework applicable to any combination of system and service parameters, such as finite field sizes, lengths of the data stream and level of sparsity. The deviation of the proposed approximation from Monte Carlo simulations is negligible, improving significantly on the state of the art performance bounds.",
keywords = "broadcast communication, Decoding, Encoding, Junctions, Monte Carlo methods, multicast communications, Network coding, Reliability, Sparse matrices, Sparse random network coding, Stein-Chen method",
author = "Suzie Brown and Oliver Johnson and Andrea Tassi",
year = "2018",
month = "5",
day = "12",
doi = "10.1109/TVT.2018.2790436",
language = "English",
volume = "67",
pages = "4677--4682",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
number = "5",

}

RIS - suitable for import to EndNote

TY - JOUR

T1 - Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

AU - Brown, Suzie

AU - Johnson, Oliver

AU - Tassi, Andrea

PY - 2018/5/12

Y1 - 2018/5/12

N2 - Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kind of coding technique. In particular, by exploiting the Stein--Chen method, we provide a novel and general performance framework applicable to any combination of system and service parameters, such as finite field sizes, lengths of the data stream and level of sparsity. The deviation of the proposed approximation from Monte Carlo simulations is negligible, improving significantly on the state of the art performance bounds.

AB - Ultra-reliable Point-to-Multipoint (PtM) communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse Random Linear Network Coding (RLNC) techniques have been widely employed to provide an efficient way to improve the reliability of broadcast and multicast data streams. This paper addresses the pressing concern of providing a tight approximation to the probability of a user recovering a data stream protected by this kind of coding technique. In particular, by exploiting the Stein--Chen method, we provide a novel and general performance framework applicable to any combination of system and service parameters, such as finite field sizes, lengths of the data stream and level of sparsity. The deviation of the proposed approximation from Monte Carlo simulations is negligible, improving significantly on the state of the art performance bounds.

KW - broadcast communication

KW - Decoding

KW - Encoding

KW - Junctions

KW - Monte Carlo methods

KW - multicast communications

KW - Network coding

KW - Reliability

KW - Sparse matrices

KW - Sparse random network coding

KW - Stein-Chen method

UR - http://www.scopus.com/inward/record.url?scp=85040539780&partnerID=8YFLogxK

U2 - 10.1109/TVT.2018.2790436

DO - 10.1109/TVT.2018.2790436

M3 - Article

VL - 67

SP - 4677

EP - 4682

JO - IEEE Transactions on Vehicular Technology

JF - IEEE Transactions on Vehicular Technology

SN - 0018-9545

IS - 5

ER -