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.

翻译：我们考虑了对一个无记忆频道的能力进行上限估计的问题,该频道的频道法和连续输出字母未知。我们建议采用新的数据驱动算法,在以最小值取代输入分布最大化时,利用双向能力代表法,在频道输出的引用分布上,以最小值取代输入分布最大化。为了有效地计算有条件频道和引用分布之间所需的最大差异,我们使用一个经过修改的相互信息神经测算器,将频道输入作为附加参数。我们用数字来评估我们在不同无记忆频道上的做法,并用经验显示估计的上界与频道能力或最知名的下界紧密结合。