Generalized Automatic Modulation Classification Under Non-Gaussian Noise
with Varying SNR Conditions: A CNN Enable Method
Abstract
Automatic modulation classification (AMC) is an critical step to
identify signal modulation types so as to enable more accurate
demodulation in the non-cooperative scenarios. Convolutional neural
network (CNN)-based AMC is believed as one of the most promising methods
with great classification accuracy. However, the conventional CNN-based
methods are lack of generality capabilities under time-varying
signal-to-noise ratio (SNR) conditions, because these methods are merely
trained on specific datasets and can only work at the corresponding
condition. In this paper, a novel CNN-based generalized AMC method is
proposed, and a more realistic scenario is considered, including white
non-Gaussian noise and synchronization error. Its generalization
capability stems from the mixed datasets under varying noise scenarios,
and the CNN can extract common features from these datasets. Simulation
results show that our proposed architecture can achieve higher
robustness and generalization than the conventional ones.