  In the context of wireless communications, we propose a deep learning
approach to learn the mapping from the instantaneous state of a frequency
selective fading channel to the corresponding frame error probability (FEP) for
an arbitrary set of transmission parameters. We propose an abstract model of a
bit interleaved coded modulation (BICM) orthogonal frequency division
multiplexing (OFDM) link chain and show that the maximum likelihood (ML)
estimator of the model parameters estimates the true FEP distribution. Further,
we exploit deep neural networks as a general purpose tool to implement our
model and propose a training scheme for which, even while training with the
binary frame error events (i.e., ACKs / NACKs), the network outputs converge to
the FEP conditioned on the input channel state. We provide simulation results
that demonstrate gains in the FEP prediction accuracy with our approach as
compared to the traditional effective exponential SIR metric (EESM) approach
for a range of channel code rates, and show that these gains can be exploited
to increase the link throughput.
