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.
翻译:我们考虑线性网络错误校正(LNEC), 当在已知地形的通信网络边缘发生错误时, 我们考虑线性网络错误校正(LNEC) 编码。 在本文中, 我们首先重新审视和探索 LNEC 编码框架, 然后统一两个众所周知的 LNEC 编码方法。 此外, 通过对 LNEC 编码框架制定图形理论理论方法, 我们大大加强了LNEC 编码错误校正能力在汇点最低距离方面的特征描述。 在 LNEC 编码中, LNEC 代码存在所需的最低字段尺寸, 特别是LNEC 最大距离 separble(MDS) 代码(LNEC 最大距离 separble (MDS) 代码是最重要的最优化代码类型, 是一个开放的问题, 不仅具有理论意义, 而且还具有实际重要性。 因为它与在计算复杂性和储存要求方面执行的编码计划密切相关, 我们的上层和直线性算法中, 一种精细的计算能力 。