  Modulation recognition using deep neural networks has shown promising
advantages over conventional algorithms. However, most existing research
focuses on single receive antenna. In this paper, two end-to-end feature
learning deep architectures are introduced for modulation recognition with
multiple receive antennas. The first is based on multi-view convolutional
neural network by treating signals from different receive antennas as different
views of a 3D object and designing the location and operation of view-pooling
layer that are suitable for feature fusion of multi-antenna signals.
Considering that the instantaneous SNRs could be different among receive
antennas in wireless communications, we further propose weight-learning
convolutional neural network which uses a weight-learning module to
automatically learn the weights for feature combing of different receive
antennas to perform end-to-end feature learning of multi-antenna signals.
Results show that both end-to-end feature learning deep architectures
outperform the existing algorithm, and the proposed weight-learning
convolutional neural network achieves the best performance.
