Signals Recognition by CNN Based on Attention Mechanism

Abstract

Automatic modulation recognition is a key technology in non-collaborative communication. However, it is affected by complex electromagnetic environments, leading to low recognition accuracy. To address this problem, this paper develops a ResNext signal recognition model based on an attention mechanism. Firstly, a channel, including additive Gaussian white noise (AWGN), Rician multipath fading, and clock offset, is created to simulate the complex electromagnetic environment, and transmission-impaired modulated signals with various signal-to-noise ratios (SNRs) are synthesized as a dataset. Secondly, using parallel stacked residual blocks of the same topology, instead of the residual blocks of ResNet, and introducing the attention layer (CBAM), the types of feature extraction are enriched without significantly increasing the parameter order of magnitude and avoiding the over-fitting phenomenon caused by depth deepening. The results show that the signal recognition method, based on the improved neural network framework, outperformed other deep learning methods, and the recognition rate obtained of 10 different modulation types of signals was above 90% at SNRs greater than 0 dB. The proposed signal recognition method achieved accurate recognition in complex electromagnetic environments.