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.

arXiv commentAccepted for publication on IEEE Transactions on Vehicular Technology

References in corpus (4)

• Network Coding Meets Multimedia: a Review
• Resource Allocation Frameworks for Network-coded Layered Multimedia Multicast Services
• Analysis and Optimization of Sparse Random Linear Network Coding for Reliable Multicast Services
• Improved bounds on the decoding failure probability of network coding over multi-source multi-relay networks