# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 4 Modulation Recognition Based on Deep Learning in Small Sample Environment
### Small-Sample AMR Algorithm Combining Deep Learning and Transfer Learning  
Transfer learning is good at applying learned knowledge or models to other domains to solve similar problems in different domains [103]. At present, there are neural networks designed for small sample problems in the fields of machine vision and handwritten digit recognition. Therefore, some scholars have migrated deep neural networks in these fields to the field of modulation recognition to solve the problem of poor modulation recognition accuracy in a small sample environment. Some scholars also transfer the parameters trained by other networks to the neural network used for modulation recognition as the initial parameters, to improve the recognition accuracy in a small sample environment. In this section, this paper will briefly describe the small-sample AMR algorithm combined with deep learning and transfer learning.  
In order to solve the problem of small samples, [PERSON] et al. introduced the CapsNet network in the field of handwritten digit recognition into the field of AMR in their work [104]. Experiments were carried out using 5% of the signal data of the modulated signal dataset RML2016.04c. The results show that when the signal-to-noise ratio is greater than 2 dB, the classification accuracy of AMR-CapsNet is greater than 80%. When experimenting with 3% of the signal data of the dataset and the signal-to-noise ratio is greater than 4 dB, the recognition accuracy of the AMR-CapsNet network exceeds 80%, and the average recognition rate is higher than that of CNN. The authors of [105] proposed an adversarial transfer learning architecture (ATLA), which utilizes adversarial training to reduce the differences between data distributions and achieve transfer learning. Experiments show that the ATLA model can achieve a recognition accuracy of 80%, indicating that the algorithm is superior to the existing parameter transfer methods in improving the migration ability and expanding the tolerance of distribution differences.  
The authors of [106] proposed a new convolutional neural network based on transfer learning. The authors take the radar signals of three types of intrapulse modulation as the source domain and use the CNN to train the signals in the source domain to obtain relevant parameters; then transfers the trained parameters and models to the target domain with 450 radar signals for training. When the SNR is greater than \(-1\), 90% classification accuracy can be achieved. The authors of [107] proposed a modulation recognition method based on data transfer learning. The authors use the coefficient autoencoder to learn the noise signal in water, thus constructing the received signal dataset and using the constructed dataset to train the neural network. Finally, the trained network is used to identify the modulation categories of real underwater acoustic communication signals. The authors of [108] proposed a new modulation recognition method for OFDM-VLC systems, firstly using a large number of ImagNet images to pre-train AlexNet and GoogleNet, then using the weights in the pre-training stage to retrain the constellation diagram of the small sample signal, and finally completing the modulation identification of the signal.  
In this section, we introduced the modulation recognition problem in the small sample environment, mainly using data augmentation and transfer learning methods to solve it. However, the data enhancement method cannot guarantee the quality of the data, which may lead to a decrease in the recognition accuracy. The combination of transfer learning and deep learning can transfer models and parameters from other fields to modulation recognition, but there are fewer models specifically for modulation recognition, and generalization is not strong enough, so how to design a small sample recognition model in the field of modulation recognition is still an important research topic in the future.