  Nowadays, we mainly use various convolution neural network (CNN) structures
to extract features from radio data or spectrogram in AMR. Based on expert
experience and spectrograms, they not only increase the difficulty of
preprocessing, but also consume a lot of memory. In order to directly use
in-phase and quadrature (IQ) data obtained by the receiver and enhance the
efficiency of network extraction features to improve the recognition rate of
modulation mode, this paper proposes a new network structure called Fully Dense
Neural Network (FDNN). This network uses residual blocks to extract features,
dense connect to reduce model size, and adds attentions mechanism to
recalibrate. Experiments on RML2016.10a show that this network has a higher
recognition rate and lower model complexity. And it shows that the FDNN model
with dense connections can not only extract features effectively but also
greatly reduce model parameters, which also provides a significant contribution
for the application of deep learning to the intelligent radio system.
