# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 1 Introduction
Likelihood-based methods have theoretical optimality, but the amount of computation is extremely large [8], while feature-based methods require manual feature extraction, resulting in recognition results that rely heavily on experts' feature extraction experience. Therefore, these two recognition methods are no longer suitable for increasingly complex communication systems. In the past few years, scholars in the field of communication have applied typical networks in deep learning, such as convolutional neural networks [9] and recurrent neural networks [10], etc. AMR has shown deep learning-based modulation recognition algorithms perform better than traditional modulation identification methods. Since then, many researchers have started to study automatic modulation recognition algorithms based on deep learning. [PERSON] et al. combined two convolutional neural networks to complete modulation recognition [11]. In the literature [12], the authors used deep belief networks (DBN) and spiking neural networks (SNN) to successfully complete the AMR task under a low signal-to-noise ratio. The basic idea of the DL-based AMR algorithm is to input a large number of labeled modulation signal datasets (currently commonly used open-source modulation signal datasets, RadioML2016.10a, RadioML2016.10b, RadioML2016.04c, RadioML2018.10A, and HisarMod2019.1) into the neural network. In the network, the neural network learns and extracts the features of the signal in the labeled dataset [13], and finally completes the classification according to the features. The entire process does not require a manual signal feature extraction [14] and can achieve high recognition accuracy. It can be seen that the DL-based AMR method has the following advantages: less expert knowledge, strong feature extraction ability, and high recognition accuracy. Therefore, many scholars have designed different deep learning networks for recognition. It is worth noting that although the DL-based AMR method performs well, it also has limitations: it is well known that deep learning models can only achieve good classification results with huge datasets. If the dataset is too small, then the depth of the learning model is prone to overfitting, resulting in a poor final learning classification effect. In general, if the number of samples in each category in the dataset is only tens or hundreds, then the data is a small sample dataset [15]. For modulated signals, if a modulated signal has only a few hundred samples, the modulated signal dataset is a small sample dataset. In the field of communication, there are many occasions, such as deep-sea communication, radar communication, etc., that cannot obtain huge, labeled datasets, which will lead to poor performance of DL-based AMR algorithms. Therefore, to solve the problem of low modulation recognition accuracy in a small sample environment, researchers need to develop an AMR algorithm suitable for a small sample environment. At present, some scholars have begun to study the DL-based AMR algorithm under the condition of small samples. The authors of [16] proposed an easy-to-train meta-learner, Meta-SGD, which can achieve better learning results with a few data samples. [PERSON] et al. used the transfer learning method in their work [17] to improve the modulation recognition accuracy in the case of small samples. According to the above introduction, we know that the modulation recognition algorithm based on deep learning has a large gap when the amount of sample data is sufficient or insufficient. At present, there are many algorithms in these two cases, so it is of great significance to summarize the DL-based AMR algorithms in these two cases for subsequent research.