论文标题
数据驱动的容量上限估计
Data-Driven Estimation of Capacity Upper Bounds
论文作者
论文摘要
我们考虑使用未知通道定律和连续输出字母的无内存通道的上限估算上限的问题。提出了一种新型的数据驱动算法，该算法利用了容量的双重表示，其中最大化输入分布的最大化被最小化，而不是通道输出上的参考分布。为了有效地计算条件通道和参考分布之间所需的差异最大化，我们使用修改后的互信息神经估计器，该神经估计器将通道输入作为附加参数。我们在数值上评估了不同的无内存通道上的方法，并经验表明，估计的上限与通道容量或最著名的下限紧密融合。
We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm is proposed that exploits the dual representation of capacity where the maximization over the input distribution is replaced with a minimization over a reference distribution on the channel output. To efficiently compute the required divergence maximization between the conditional channel and the reference distribution, we use a modified mutual information neural estimator that takes the channel input as an additional parameter. We numerically evaluate our approach on different memoryless channels and show empirically that the estimated upper bounds closely converge either to the channel capacity or to best-known lower bounds.
