Linear Network Error Correction Coding: A Revisit

TL;DR

This paper revisits linear network error correction coding, unifies existing approaches, and introduces a graph-theoretic method to better characterize error correction capabilities and improve bounds on field size requirements.

Contribution

It unifies two LNEC coding approaches, develops a graph-theoretic framework, and provides an improved, network topology-dependent upper bound on the field size for LNEC codes.

Findings

Enhanced characterization of error correction in LNEC codes.

Significant improvement in upper bounds on field size.

Development of a linear-time algorithm for bound computation.

Abstract

We consider linear network error correction (LNEC) coding when errors may occur on edges of a communication network of which the topology is known. In this paper, we first revisit and explore the framework of LNEC coding, and then unify two well-known LNEC coding approaches. Furthermore, by developing a graph-theoretic approach to the framework of LNEC coding, we obtain a significantly enhanced characterization of the error correction capability of LNEC codes in terms of the minimum distances at the sink nodes. In LNEC coding, the minimum required field size for the existence of LNEC codes, in particular LNEC maximum distance separable (MDS) codes which are a type of most important optimal codes, is an open problem not only of theoretical interest but also of practical importance, because it is closely related to the implementation of the coding scheme in terms of computational complexity and storage requirement. By applying the graph-theoretic approach, we obtain an improved upper bound on the minimum required field size. The improvement over the existing results is in general significant. The improved upper bound, which is graph-theoretic, depends only on the network topology and requirement of the error correction capability but not on a specific code construction. However, this bound is not given in an explicit form. We thus develop an efficient algorithm that can compute the bound in linear time. In developing the upper bound and the efficient algorithm for computing this bound, various graph-theoretic concepts are introduced. These concepts appear to be of fundamental interest in graph theory and they may have further applications in graph theory and beyond.