arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1607.02822cs.ITmath.IT
收藏

相关源网络的容量界及分布的熵刻画

Capacity Bounds for Networks with Correlated Sources and Characterisation of Distributions by Entropies

Satyajit Thakor, Terence Chan, Alex Grant

摘要:

表征网络的容量区域可能非常困难。即使有独立的源,确定容量区域也可能像描述所有信息不平等的公开问题一样困难。文献中的大多数可计算外界都是线性规划界的放宽,其中涉及与源和链接消息相关的随机变量的熵函数。当来源不独立时,问题就更加复杂。将线性规划界扩展到具有相关源的网络在很大程度上是开放的。源依赖性通常通过联合概率分布来指定,扩展线性规划边界的主要挑战之一是通过熵函数来表征任意依赖性的困难(或不可能)。本文通过回答熵函数如何很好地描述信源之间的相关性来解决这个问题。我们表明,通过使用精心选择的辅助随机变量,特征可以是相当“准确的”使用这样的辅助随机变量,我们还给出了隐式和显式的外部界限的相关源网络的容量。通过香农熵函数表征相关性或联合分布也适用于其他信息度量,例如Renyi熵和Tsallis熵。

英文摘要:

Characterising the capacity region for a network can be extremely difficult. Even with independent sources, determining the capacity region can be as hard as the open problem of characterising all information inequalities. The majority of computable outer bounds in the literature are relaxations of the Linear Programming bound which involves entropy functions of random variables related to the sources and link messages. When sources are not independent, the problem is even more complicated. Extension of Linear Programming bounds to networks with correlated sources is largely open. Source dependence is usually specified via a joint probability distribution, and one of the main challenges in extending linear program bounds is the difficulty (or impossibility) of characterising arbitrary dependencies via entropy functions. This paper tackles the problem by answering the question of how well entropy functions can characterise correlation among sources. We show that by using carefully chosen auxiliary random variables, the characterisation can be fairly "accurate" Using such auxiliary random variables we also give implicit and explicit outer bounds on the capacity of networks with correlated sources. The characterisation of correlation or joint distribution via Shannon entropy functions is also applicable to other information measures such as Renyi entropy and Tsallis entropy.

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。

↑