Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

Ultrareliable point-to-multipoint communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse random linear network coding techniques have been widely employed to provide an efficient way to improve the reliability of broadca...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 67; no. 5; pp. 4677 - 4682
Main Authors: Brown, Suzie, Johnson, Oliver, Tassi, Andrea
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Approximation
broadcast communication
Coding
Computer simulation
Data transmission
Decoding
Encoding
Fields (mathematics)
Junctions
Mathematical analysis
Monte Carlo methods
Multicast
multicast communications
Network coding
Network reliability
Reliability
Sparse matrices
Sparse random network coding
State of the art
Stein–Chen method
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:Ultrareliable point-to-multipoint communications are expected to become pivotal in networks offering future dependable services for smart cities. In this regard, sparse random linear network coding 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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2018.2790436