A Modulation Recognition Method Based on Lightweight Neural Networks
In recent years,the application of deep learning to the field of modulation recognition is a popular direction,but the constantly complex network structure puts great pressure on the hard-ware equipment for raising the recognition accuracy.This paper proposes a method using Mobile-NetV2 network in modulation recognition.Firstly,the dataset of 11 kinds of modulation signals is generated,then MobileNetV2 network is used to train the modulation recognition model,and final-ly the classification output of 11 kinds of modulation recognition is performed through the fully connected layer.The experiment shows that the recognition rate of MobileNetV2 reaches more than 95％,which is about 5％higher than that of the two convolutional networks in the experimen-tal comparison,and the number of network parameters is greatly reduced,as well as the training time is controlled,which reduces the requirements to hardware devices.This method has research value and significance for the subsequent application of lightweight deep learning networks to mod-ulation recognition.
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