# Reliability of Broadcast Communications Under Sparse Random Linear Network Coding

## TL;DR

This paper develops a novel, accurate approximation framework for the probability of successful data recovery in sparse Random Linear Network Coding, enhancing reliability analysis for future smart city broadcast systems.

## Contribution

It introduces a Stein--Chen based performance framework that provides tight probability approximations applicable to various system parameters, surpassing existing bounds.

## Key findings

- Approximation closely matches Monte Carlo simulations
- Significant improvement over existing performance bounds
- Applicable to diverse system configurations

## 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.