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Sum-set inequalities from aligned image sets: Instruments for robust GDoF bounds

机译:对齐图像集的总和不等式:可靠的GDoF边界的工具

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摘要

We present sum-set inequalities specialized to the generalized degrees of freedom (GDoF) framework. These are information theoretic lower bounds on the entropy of bounded density linear combinations of discrete, power-limited dependent random variables in terms of the joint entropies of arbitrary linear combinations of new random variables that are obtained by power level partitioning of the original random variables. The bounds are useful instruments to obtain GDoF characterizations for wireless interference networks, especially with multiple antenna nodes, subject to arbitrary channel strength and channel uncertainty levels.
机译:我们提出专门针对广义自由度(GDoF)框架的和集不等式。这些是关于离散的,幂受限的随机变量的有界密度线性组合的熵的信息理论下界,这取决于通过对原始随机变量进行功率级划分而获得的新随机变量的任意线性组合的联合熵。边界是获得无线干扰网络(尤其是具有多个天线节点的无线干扰网络)的GDoF表征的有用工具,受制于任意信道强度和信道不确定性水平。

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