  Deep learning (DL) based autoencoder is a promising architecture to implement
end-to-end communication systems. One fundamental problem of such systems is
how to increase the transmission rate. Two new schemes are proposed to address
the limited data rate issue: adaptive transmission scheme and generalized data
representation (GDR) scheme. In the first scheme, an adaptive transmission is
designed to select the transmission vectors for maximizing the data rate under
different channel conditions. The block error rate (BLER) of the first scheme
is 80% lower than that of the conventional one-hot vector scheme. This implies
that higher data rate can be achieved by the adaptive transmission scheme. In
the second scheme, the GDR replaces the conventional one-hot representation.
The GDR scheme can achieve higher data rate than the conventional one-hot
vector scheme with comparable BLER performance. For example, when the vector
size is eight, the proposed GDR scheme can double the date rate of the one-hot
vector scheme. Besides, the joint scheme of the two proposed schemes can create
further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for
these DL-based communication systems. Numerical results show that training the
autoencoder using data set with various SNR values can attain robust BLER
performance under different channel conditions.
