  Channel capacity plays a crucial role in the development of modern
communication systems as it represents the maximum rate at which information
can be reliably transmitted over a communication channel. Nevertheless, for the
majority of channels, finding a closed-form capacity expression remains an open
challenge. This is because it requires to carry out two formidable tasks a) the
computation of the mutual information between the channel input and output, and
b) its maximization with respect to the signal distribution at the channel
input. In this paper, we address both tasks. Inspired by implicit generative
models, we propose a novel cooperative framework to automatically learn the
channel capacity, for any type of memory-less channel. In particular, we
firstly develop a new methodology to estimate the mutual information directly
from a discriminator typically deployed to train adversarial networks, referred
to as discriminative mutual information estimator (DIME). Secondly, we include
the discriminator in a cooperative channel capacity learning framework,
referred to as CORTICAL, where a discriminator learns to distinguish between
dependent and independent channel input-output samples while a generator learns
to produce the optimal channel input distribution for which the discriminator
exhibits the best performance. Lastly, we prove that a particular choice of the
cooperative value function solves the channel capacity estimation problem.
Simulation results demonstrate that the proposed method offers high accuracy.
