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Linear Fractional Network Coding and Representable Discrete Polymatroids

机译:线性分式网络编码与可表示离散多拟阵

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A linear Fractional Network Coding (FNC) solution over F_q is a linear network coding solution over F_q in which the message dimensions need not necessarily be the same and need not be the same as the edge vector dimension. Scalar linear network coding, vector linear network coding are special cases of linear FNC. In this paper, we establish the connection between the existence of a linear FNC solution for a network over F_q and the representability over F_q of discrete polymatroids, which are the multi-set analogue of matroids. All previously known results on the connection between the scalar and vector linear solvability of networks and representations of matroids and discrete polymatroids follow as special cases. An algorithm is provided to construct networks which admit FNC solution over F_q, from discrete polymatroids representable over F_q. Example networks constructed from discrete polymatroids using the algorithm are provided, which do not admit any scalar and vector solution, and for which FNC solutions with the message dimensions being different provide a larger throughput than FNC solutions with the message dimensions being equal.
机译:F_q上的线性分数网络编码(FNC)解决方案是F_q上的线性网络编码解决方案,其中消息维度不必相同,也不必与边缘向量维度相同。标量线性网络编码、矢量线性网络编码是线性模糊神经网络的特例。在本文中,我们建立了F_q上网络的线性FNC解的存在性与离散多面体的F_q上的可表示性之间的联系,后者是拟阵的多集类似物。关于网络的标量线性可解性和向量线性可解性与拟阵和离散多拟阵的表示之间关系的所有已知结果都是特例。给出了一种算法,用可表示在F_q上的离散多面体构造网络,该算法允许F_q上的FNC解。给出了用该算法由离散多面体构造的示例网络,该网络不允许任何标量和向量解,对于这种情况,消息维度不同的FNC解决方案比消息维度相同的FNC解决方案提供更大的吞吐量。

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