  In this paper, the problem of determining the capacity of a communication
channel is formulated as a cooperative game, between a generator and a
discriminator, that is solved via deep learning techniques. The task of the
generator is to produce channel input samples for which the discriminator
ideally distinguishes conditional from unconditional channel output samples.
The learning approach, referred to as cooperative channel capacity learning
(CORTICAL), provides both the optimal input signal distribution and the channel
capacity estimate. Numerical results demonstrate that the proposed framework
learns the capacity-achieving input distribution under challenging non-Shannon
settings.
