  Upcoming modern communications are based on 5G specifications and aim at
providing solutions for novel vertical industries. One of the major changes of
the physical layer is the use of Low-Density Parity-Check (LDPC) code for
channel coding. Although LDPC codes introduce additional computational
complexity compared with the previous generation, where Turbocodes where used,
LDPC codes provide a reasonable trade-off in terms of complexity-Bit Error Rate
(BER). In parallel to this, Deep Learning algorithms are experiencing a new
revolution, specially to image and video processing. In this context, there are
some approaches that can be exploited in radio communications. In this paper we
propose to use Autoencoding Neural Networks (ANN) jointly with a Deep Neural
Network (DNN) to construct Autoencoding Deep Neural Networks (ADNN) for
demapping and decoding. The results will unveil that, for a particular BER
target, $3$ dB less of Signal to Noise Ratio (SNR) is required, in Additive
White Gaussian Noise (AWGN) channels.
