  Deep Learning has been widely applied in the area of image processing and
natural language processing. In this paper, we propose an end-to-end
communication structure based on autoencoder where the transceiver can be
optimized jointly. A neural network roles as a combination of channel encoder
and modulator. In order to deal with input sequences parallelly, we introduce
block scheme, which means that the autoencoder divides the input sequence into
a series of blocks. Each block contains fixed number of bits for encoding and
modulating operation. Through training, the proposed system is able to produce
the modulated constellation diagram of each block. The simulation results show
that our autoencoder performs better than other autoencoder-based systems under
additive Gaussian white noise (AWGN) and fading channels. We also prove that
the bit error rate (BER) of proposed system can achieve an acceptable range
with increasing the number of symbols.
