  In this paper, a deep convolutional neural network-based symbol detection and
demodulation is proposed for generalized frequency division multiplexing with
index modulation (GFDM-IM) scheme in order to improve the error performance of
the system. The proposed method first pre-processes the received signal by
using a zero-forcing (ZF) detector and then uses a neural network consisting of
a convolutional neural network (CNN) followed by a fully-connected neural
network (FCNN). The FCNN part uses only two fully-connected layers, which can
be adapted to yield a trade-off between complexity and bit error rate (BER)
performance. This two-stage approach prevents the getting stuck of neural
network in a saddle point and enables IM blocks processing independently. It
has been demonstrated that the proposed deep convolutional neural network-based
detection and demodulation scheme provides better BER performance compared to
ZF detector with a reasonable complexity increase. We conclude that
non-orthogonal waveforms combined with IM schemes with the help of deep
learning is a promising physical layer (PHY) scheme for future wireless
networks
