# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning
### Recent Research Trends of AMC Based on DL
The experimental results showed that the RT of constellation diagrams could improve the performance of the classifier and the recognition accuracy at low SNRs. Soon, [PERSON] et al. [26] conducted a more in-depth study in this direction. An AMC approach based on 2D transforms and CNN was proposed. The constellation diagrams were processed using three different 2D transforms. These transforms were RT, the curvelet transform and the phase congruency (PC). The effect of using different transformed constellation diagrams on the performance of AMC was analyzed experimentally. [PERSON] et al. [103] proposed an LPI radar signal recognition method based on dual channel CNN and feature fusion. The author used the wavelet transform method to transform the signal into a time-frequency image and carried out gray processing on the time-frequency image, then input it into the dual-channel CNN model. This model could extract two features from the signal time-frequency diagram, namely, the directional gradient and the depth feature histogram. Finally, the two features were combined for classification. The recognition rate could reach more than 95% when the SNR was 6 dB. The second category is about feature extraction. Recently, many studies have added transformer or attention mechanism to the basic network. The transformer can extract the temporal correlation features between signals and improve the recognition accuracy of neural networks. The attention mechanism can make the model focus on the relevant characteristics of signals and accelerate the training process. It is able to judge the importance of each feature, select important signal features for processing and improve the efficiency of the neural network. In 2022, [PERSON] et al. [28] proposed a new transformer based DNN-MCformer for AMC of complex radio signals. MCformer used the convolution layer and self-attention mechanism. It significantly reduced the number of parameters and achieved the most advanced performance. Experiments showed the excellent performance of the architecture based on MCformer. [PERSON] et al. [55] proposed a time-frequency attention mechanism for automatic modulation recognition based on CNN. The time-frequency attention mechanism was designed to learn which channel, frequency and time information is more meaningful for modulation recognition in CNN. Experiment results demonstrated that the proposed attention mechanism required a similar inference time as the other methods and fewer learned parameters than IQ-CNN and CLDNN. The third category concerns hardware implementation. Although most previous studies on AMC have achieved good results, most of them are still in the simulation stage. Recently, many scholars began studying AMC hardware implementation based on DL. In 2022, [PERSON] et al. [104] designed AMC schemes based on CNN for the complex time radio signal domain and implemented them on the FPGA platform. Based on the training mechanism of iterative pruning, the model size on the hardware was reduced and the overall accuracy was kept above a certain threshold. The proposed scheme achieved an accuracy of at least 1.4% higher than the baseline and only took 40% of the hardware resources. The model achieved a real-time throughput of 527k classifications per second, with a delay of 7.5 \(\upmu\)s.