Modulation Format Identification in a Satellite to Ground Optical Wireless Communication Systems Using a Convolution Neural Network  
[PERSON]  
1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences,  
Dongnanhu Road 3888, Changchun 130033, China; [EMAIL_ADDRESS] (Y.G. );  
1 [EMAIL_ADDRESS] (X.L. );  
2 [EMAIL_ADDRESS] (S.M. );  
3f_f_-6811106163.com (T.J.);  
4  
[PERSON]  
1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences,  
Dongnanhu Road 3888, Changchun 130033, China; [EMAIL_ADDRESS] (Y.G. );  
1 [EMAIL_ADDRESS] (X.L. );  
2 [EMAIL_ADDRESS] (S.M. );  
3f_f_-6811106163.com (T.J.);  
3f_f_-6811106163. links are beginning to take over RF communication links in the field of high bandwidth applications [2]. The requirement tendency of forthcoming satellite data transmission has grown to become a critical boost to the progress of communication technology. Gigantic amounts of information and complicated channel circumstances require high data speed and flexibility at the same time. FSO communication has been taking part in a more vital role in the satellite-to-ground communication step by step [3]. For the flexibility of services and applications, the next-generation FSO network is expected to adjust the modulation format dynamically according to the link conditions and terminal equipment configuration to meet the different requirements of the terminal system and service [4; 5]. The traditional method needs to spend time and data processing prior information from the transmitter in the recognition process, which is a great waste in the short connection time of a satellite-to-earth link. To promote demodulation efficiency, it is a challenging requirement for an FSO receiver to realize blind modulation format identification (MFI). Over the last few years, several classical MFI techniques for optical communication have been proposed [6; 7; 8]. Machine learning (ML) is a powerful interdisciplinary subject combining mathematics, computing, and biological sciences. In recent years, it has been successfully applied in the fields of pattern recognition, computer vision, personalized technology, and data analysis and mining [9]. Recently, techniques from ML have also performed well in some intelligent expansion directions in the field of optical communication, such as optical performance monitoring (OPM), MFI, and nonlinear impairments compensation [4; 9; 10]. Compared with traditional methods, the advantage of ML methods for MFI is that it completes the training of the model before practical application [11]. It only needs to let the model know all possible modulation formats during training. In practical use, it can quickly obtain reliable recognition results without sacrificing the processing of prior information. Before that, if the results are not satisfactory in the training process, the model parameters can be adjusted repeatedly before actual communication without adjusting in the time after the actual link is established. As a representative algorithm of machine learning, convolutional neural networks (CNN) have made great achievements in image recognition. Sparse connectivity allows CNN to recognize the local features of input images without all connected feature engineering, which makes CNN distinctly efficient from conventional machine learning techniques [12]. Also, the results of paper [9] show that CNN achieves optimal accuracy and is significantly superior to other ML methods. However, previous research on MFI has focused mainly on radio and fiber-optic communication networks, and not as much work has been done for MFI in FSO communication networks [13; 14]. In this paper, we focus on the performance of the CNN algorithm in modulation format recognition in an FSO communication system. Here, we mainly illustrate an FSO optical communication system under the random channel condition including the strength of atmosphere turbulence and signal-to-noise ratio (SNR) of AWGN.