Research of Automatic Modulation Recognition Technology based on Deep Learning
Nanbin Liu · Applied and Computational Engineering · 2025
Automatic modulation recognition is a vital component in communication systems. It helps in recognizing and classifying signal’ modulation automatically without the acquirement of the signal’s modulation type in advance. This paper aims to provide a comprehensive exploration of the application of deep learning techniques in the field of Automatic Modulation Recognition (AMR), with a specific emphasis on the analysis and comparison of two prominent neural network architectures: Convolutional Neural Networks (CNN) and Convolutional Long Short-Term Memory Networks (CLDNN). By delving into the structural and functional aspects of these models, the paper seeks to elucidate how CNNs, with their ability to capture spatial features through convolutional layers, and CLDNNs, which enhance this capability by integrating Long Short-Term Memory (LSTM) units to handle temporal dynamics, contribute to the advancement of AMR technology. We used the DeepSig dataset: RadioML 2018.01A to evaluate them, comparing their performance in different signal-to-noise ratio scenarios. The results manifested that CLDNN performed better than CNN obviously, especially in the low SNR scenario where CLDNN reached a higher validation set accuracy.