  Deep learning (DL)-based autoencoder is a potential architecture to implement
end-to-end communication systems. In this letter, we first give a brief
introduction to the autoencoder-represented communication system. Then, we
propose a novel generalized data representation (GDR) aiming to improve the
data rate of DL-based communication systems. Finally, simulation results show
that the proposed GDR scheme has lower training complexity, comparable block
error rate performance and higher channel capacity than the conventional
one-hot vector scheme. Furthermore, we investigate the effect of
signal-to-noise ratio (SNR) in DL-based communication systems and prove that
training at a high SNR could produce a good training performance for
autoencoder.
