Abstract
Aiming at the problem of low accuracy of existing wireless communication blind recognition methods, a novel multi-channel deep learning framework based on the Convolutional Long Short-Term Memory Fully Connected Deep Neural Network (MC-CLDNN) is proposed. We fully combine the advantages of convolution neural network (CNN), gated recurrent unit (GRU) and deep neural network (DNN) in feature extraction ability to improve the efficiency of network training. Furthermore, to alleviate the problem of gradient disappearance in the network training and reduce the negative effect of pooling layer processes time series data on the subsequent sequence model, the skip connection is added to the network model. We verify the feasibility of the model based on opensource dataset RadioML2016.10a. The simulation results show that the proposed model can identify most modulation modes effectively, and has the characteristics of high recognition accuracy and strong generalization ability.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
