  Modulation classification, an intermediate process between signal detection
and demodulation in a physical layer, is now attracting more interest to the
cognitive radio field, wherein the performance is powered by artificial
intelligence algorithms. However, most existing conventional approaches pose
the obstacle of effectively learning weakly discriminative modulation patterns.
This paper proposes a robust modulation classification method by taking
advantage of deep learning to capture the meaningful information of modulation
signal at multi-scale feature representations. To this end, a novel
architecture of convolutional neural network, namely Chain-Net, is developed
with various asymmetric kernels organized in two processing flows and
associated via depth-wise concatenation and element-wise addition for
optimizing feature utilization. The network is evaluated on a big dataset of 14
challenging modulation formats, including analog and high-order digital
techniques. The simulation results demonstrate that Chain-Net robustly
classifies the modulation of radio signals suffering from a synthetic channel
deterioration and further performs better than other deep networks.
