  In this paper, we present a deep learning based wireless transceiver. We
describe in detail the corresponding artificial neural network architecture,
the training process, and report on excessive over-the-air measurement results.
We employ the end-to-end training approach with an autoencoder model that
includes a channel model in the middle layers as previously proposed in the
literature. In contrast to other state-of-the-art results, our architecture
supports learning time synchronization without any manually designed signal
processing operations. Moreover, the neural transceiver has been tested over
the air with an implementation in software defined radio. Our experimental
results for the implemented single antenna system demonstrate a raw bit-rate of
0.5 million bits per second. This exceeds results from comparable systems
presented in the literature and suggests the feasibility of high throughput
deep learning transceivers.
