Abstract:

As an intermediate step of signal detection and demodulation, automatic modulation recognition played a momentous role in wireless communication system.Aiming at the low recognition accuracy of existing automatic modulation recognition methods, a bimodal hybrid neural network (BHNN) was proposed, which utilized complementary gain information contained in multiple modes to enrich feature dimensions.The improved residual network was connected in parallel with the bidirectional gated loop unit to construct a bimodal hybrid neural network model, and the spatial and temporal features of the signal were extracted respectively.The DropBlock regularization algorithm was introduced to effectively suppress the influence of over fitting, gradient disappearance and gradient explosion on the recognition accuracy in the process of network training.Using bimodal data input, the spatial and temporal characteristics of signals were fully utilized, and the network depth was reduced through parallel connection.The model convergence was accelerated, and the recognition accuracy of modulated signals was improved.In order to verify the effectiveness of the model, two public datasets were used to simulate the model.The results show that BHNN has high recognition accuracy and strong stability on the two datasets, and the recognition accuracy can reach 89% and 93.63% respectively under high signal-to-noise ratio.

Key words: modulation recognition, bimodal hybrid neural network, DropBlock regularization, ResNet, BiGRU