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arXiv 1901.04820cs.ITmath.IT
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线性网络编码:改变信息维数对特征集的影响

Linear Network Coding: Effects of Varying the Message Dimension on the Set of Characteristics

Niladri Das, Brijesh Kumar Rai

摘要:

已知向量线性解可能存在当且仅当有限域的特征属于某个素数集合。但是,增加消息维度是否可以使网络向量在更大的特征集上线性可解?据我们所知,文献中不存在这样的网络,即对于某个消息维,它有一个向量线性解当且仅当有限域的特征属于一个集合$P$,而对于另一个消息维,它在某个特征不属于$P$的有限域上有一个向量线性解。我们已经发现,通过\textit{increasing},消息维度仅通过$1$,存在向量线性解的特征集可以任意地变大。然而,有些令人惊讶的是,我们还发现,通过\textit{decreasing},消息维度仅通过$1$,存在向量线性解的特征集可以任意变大。作为这些发现的结果,我们证明了两个结果:(i)环可能优于有限域在实现一个标量线性解决方案在一个较小的字母表,(ii)$m_1$和$m_2$维向量线性解决方案的存在性保证了一个$(m_1 + m_2)$维向量线性解决方案的存在,只有当$m_1$和$m_2$维向量线性解决方案存在于同一有限域。

英文摘要:

It is known a vector linear solution may exist if and only if the characteristic of the finite field belongs to a certain set of primes. But, can increasing the message dimension make a network vector linearly solvable over a larger set of characteristics? To the best of our knowledge, there exists no network in the literature which has a vector linear solution for some message dimension if and only if the characteristic of the finite field belongs to a set $P$, and for some other message dimension it has a vector linear solution over some finite field whose characteristic does not belong to $P$. We have found that by \textit{increasing} the message dimension just by $1$, the set of characteristics over which a vector linear solution exists may get arbitrarily larger. However, somewhat surprisingly, we have also found that by \textit{decreasing} the message dimension just by $1$, the set of characteristics over which a vector linear solution exists may get arbitrarily larger. As a consequence of these finding, we prove two more results: (i) rings may be superior to finite fields in terms of achieving a scalar linear solution over a lesser sized alphabet, (ii) existences of $m_1$ and $m_2$ dimensional vector linear solutions guarantees the existence of an $(m_1 + m_2)$-dimensional vector linear solution only if the $m_1$ and $m_2$ dimensional vector linear solutions exist over the same finite field.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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