Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

Suzie Brown, Oliver Johnson, Andrea Tassi

Research output: Contribution to journalArticle (Academic Journal)peer-review

18 Citations (Scopus)
597 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)4677-4682
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Issue number5
Early online date18 Jan 2018
Publication statusPublished - 12 May 2018


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


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