# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 1 Introduction  
In recent years, the rapid development of wireless communication technology, especially the new generation of communication technology led by 5G and 6G technology, has attracted the attention of academic and commercial circles. Among them, 5G technology can support three major application scenarios of enhanced mobile communication, ultra-reliable and low-latency, and massive machine-type communication, and promote the application of artificial intelligence products [1], which drives the development of the Internet of Things and greatly promotes economic growth, which has facilitated the production and life of human beings. With the commercialization of 5G technology, the world's major communications powers have begun preliminary studies on 6G, noting that 6G communications technology will have faster data rates, greater system capacity and wider network coverage, 6G technology can connect the communication systems of satellites, drones, and submarines to truly realize the internet of everything [2]. Since 5G and 6G communication technologies are capable of generating massive amounts of communication data and deep learning techniques in artificial intelligence are capable of learning features in massive amounts of data and accomplishing problems such as channel prediction and intelligent signal generation and processing that are difficult for humans to handle, researchers in the communication field have begun to introduce deep learning into various layers of wireless communication systems, such as the communication physical layer, communication link layer, and communication network layer. At present, Tsinghua University, Zhejiang University, Southeast University, University of Electronic Science andTechnology of China, MIT, Stanford, and other universities have carried out research on deep learning in the communication physical layer. The research content includes automatic modulation recognition based on deep learning, deep learning-based channel state estimation, prediction and feedback based on deep learning, channel coding and decoding technology based on deep learning, etc. [3]. In this article, the automatic modulation identification technology based on deep learning will be surveyed. Automatic Modulation Recognition (AMR) is an intermediate step between signal detection and signal demodulation. This technique is able to identify the type of modulation of the signal and thus obtain the information contained in the signal without knowing the system parameters. It can be seen that AMR technology is a prerequisite for demodulating signals at the receiving end, and it is also a key link in wireless communication, playing a key role in both civilian and military fields. In the civil field, AMR is mainly used for spectrum monitoring and interference identification [4]. The spectrum resources in wireless communication are very limited, but a large number of communication services occupy most of the spectrum resources, resulting in a shortage of spectrum resources, which may cause some illegal organizations to occupy the public spectrum illegally and seriously interfere with normal communication, so the spectrum resources need to be managed. AMR technology can identify the modulation method of interference signals and legitimate user signals, analyze the properties of signals, and complete the supervision of the spectrum. In the military field, AMR technology can identify the interference information and key military information sent by the enemy and help the military formulate targeted reconnaissance and anti-reconnaissance strategies. It can be seen that the development of AMR technology has important strategic significance. After decades of development of automatic modulation recognition technology, its traditional recognition methods can be roughly divided into two categories: likelihood-based methods [5; 6] and feature-based methods [7].