  End-to-end learning of a communications system using the deep learning-based
autoencoder concept has drawn interest in recent research due to its
simplicity, flexibility and its potential of adapting to complex channel models
and practical system imperfections. In this paper, we have compared the bit
error rate (BER) performance of autoencoder based systems and conventional
channel coded systems with convolutional coding (CC), in order to understand
the potential of deep learning-based systems as alternatives to conventional
systems. From the simulations, autoencoder implementation was observed to have
a better BER in 0-5 dB $E_{b}/N_{0}$ range than its equivalent half-rate
convolutional coded BPSK with hard decision decoding, and to have only less
than 1 dB gap at a BER of $10^{-5}$. Furthermore, we have also proposed a novel
low complexity autoencoder architecture to implement end-to-end learning of
coded systems in which we have shown better BER performance than the baseline
implementation. The newly proposed low complexity autoencoder was capable of
achieving a better BER performance than half-rate 16-QAM with hard decision
decoding over the full 0-10 dB $E_{b}/N_{0}$ range and a better BER performance
than the soft decision decoding in 0-4 dB $E_{b}/N_{0}$ range.
