  We extend the idea of end-to-end learning of communications systems through
deep neural network (NN)-based autoencoders to orthogonal frequency division
multiplexing (OFDM) with cyclic prefix (CP). Our implementation has the same
benefits as a conventional OFDM system, namely singletap equalization and
robustness against sampling synchronization errors, which turned out to be one
of the major challenges in previous single-carrier implementations. This
enables reliable communication over multipath channels and makes the
communication scheme suitable for commodity hardware with imprecise
oscillators. We show that the proposed scheme can be realized with
state-of-the-art deep learning software libraries as transmitter and receiver
solely consist of differentiable layers required for gradient-based training.
We compare the performance of the autoencoder-based system against that of a
state-of-the-art OFDM baseline over frequency-selective fading channels.
Finally, the impact of a non-linear amplifier is investigated and we show that
the autoencoder inherently learns how to deal with such hardware impairments.
