Reliability of Multicast Under Random Linear Network Coding

Abstract

We consider a lossy multicast network in which the reliability is provided by means of Random Linear Network Coding. Our goal is to characterise the performance of such network in terms of the probability that a source message is delivered to all destination nodes. Previous studies considered coding over large finite fields, small numbers of destination nodes or specific, often impractical, channel conditions. In contrast, we focus on a general problem, considering arbitrary field size and number of destination nodes, as well as a realistic channel. We propose a lower bound on the probability of successful delivery, which is more accurate than the approximation commonly used in the literature. In addition, we present a novel performance analysis of the systematic version of RLNC. The accuracy of the proposed performance framework is verified via extensive Monte Carlo simulations, where the impact of the network and code parameters are investigated. Specifically, we show that the mean square error of the bound for a ten-user network can be as low as 9 · 10 −5 for non-systematic RLNC.

On Intercept Probability Minimization Under Sparse Random Linear Network Coding

“…Column by column, Bob and Eve populate a K × n B and a K × n E decoding matrix M B and M E , respectively, with the coding vectors associated with the coded packets they successfully received. Bob and Eve recover the source message as soon as the defect of the decoding matrix, defined as def(M X ) = K − rank(M X ) is equal to zero, for X = B and X = E, respectively [5].…”

“…• If 0 ≤ i ≤ K, the state is and absorbing state and, hence, P i,j = 1. Proof: We consider the case as per (5). In particular, we consider the case where j = i − 1, which we can informally regard as the case where a state transition occurs horizontally, from left to right (see Fig.…”

“…In particular, we refer to a system model where achieving per-packet secrecy is not necessary if the transmitted packets are a function of a source message intended to be delivered to a legitimate receiver, and if, in order to recover the source message, a receiver has to collect at least a target number of packets [4]. As observed in [2] and [3], this assumption is met by Random Linear Network Coding (RLNC) [5], where a source node generates a stream of coded packets by linearly combining the source packets forming a source message. The legitimate receiver or an eavesdropper can recover the source message only if they successfully receive a number of linearly independent coded packets equal to the number of source packets defining the source message.…”

Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation

“…Among these devices, around ten million vehicles equipped with multiple communication systems and autonomous capabilities are expected to be rolled out into the global market [1]. In particular, there is global consensus around the fact that future automotive services for Connected and Autonomous Vehicles (CAVs) are expected to rely heavily on reliable broadband connectivity on the move [2]. This is confirmed by the National Highway Traffic Safety Administration (U.S. Department of Transportation) and the European Commission's Connected-Intelligent Transportation System (C-ITS) initiative [3], [4].…”

Secure Data Offloading Strategy for Connected and Autonomous Vehicles

“…Future cityscale ITS services will rely upon Connected and Autonomous Vehicles (CAVs) offloading their sensor data onto the Fog Computing layer via a network of Road Side Units (RSUs). The Random Linear Network Coding (RLNC) approach can be used [2]: (i) to improve the reliability of the data offloading process, and (ii) to streamline the removal of duplicated sensor data received by neighboring RSUs.…”