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arXiv 1503.04108cs.ITmath.ITmath.OC
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大字母随机信道的容量

Capacity of Random Channels with Large Alphabets

Tobias Sutter, David Sutter, John Lygeros

摘要:

我们考虑离散无记忆信道的输入字母大小$n$和输出字母大小$m$,其中$m=$ ceil $(γn)$为某个常数$γ>0$。信道转移矩阵由在被归一化之前是独立且同分布的非负随机变量$V$的条目组成,并且使得$E[(V \log V)^2]<\infty$。我们证明了在$n\to \infty$极限下,这种信道的容量几乎必然收敛于$Ent(V) / E[V]$,并且收敛于$L^2$,其中$Ent(V):= E[V\log V]-E[V] \log E[V]$表示$V$的熵。我们进一步表明,在略有不同的模型假设下,这些随机信道的容量收敛到这个渐近值在$n$指数。最后,我们提出了贝叶斯最优实验设计的背景下的应用程序。

英文摘要:

We consider discrete memoryless channels with input alphabet size $n$ and output alphabet size $m$, where $m=$ceil$(γn)$ for some constant $γ>0$. The channel transition matrix consists of entries that, before being normalised, are independent and identically distributed nonnegative random variables $V$ and such that $E[(V \log V)^2]<\infty$. We prove that in the limit as $n\to \infty$ the capacity of such a channel converges to $Ent(V) / E[V]$ almost surely and in $L^2$, where $Ent(V):= E[V\log V]-E[V] \log E[V]$ denotes the entropy of $V$. We further show that, under slightly different model assumptions, the capacity of these random channels converges to this asymptotic value exponentially in $n$. Finally, we present an application in the context of Bayesian optimal experiment design.

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