Skip to content

Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)4677-4682
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Volume67
Issue number5
Early online date18 Jan 2018
DOIs
DateAccepted/In press - 5 Jan 2018
DateE-pub ahead of print - 18 Jan 2018
DatePublished (current) - 12 May 2018

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.

    Research areas

  • broadcast communication, Decoding, Encoding, Junctions, Monte Carlo methods, multicast communications, Network coding, Reliability, Sparse matrices, Sparse random network coding, Stein-Chen method

Download statistics

No data available

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/document/8248799/. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 667 KB, PDF document

Links

DOI

View research connections

Related faculties, schools or groups