Modulation Format Identification in a Satellite to Ground Optical Wireless Communication Systems Using a Convolution Neural Network

Abstract

The satellite-to-ground communication system is a significant part of future space communication networks. The free-space optical (FSO) communication technique is a prospective solution for satellite-to-ground communication. However, atmospheric optical turbulence is a major impairment in FSO communication systems. In this paper, to improve the performance and flexibility of a satellite-to-ground laser communication system, we put forward a novel modulation format identification (MFI) technique for an FSO communication system based on a convolution neural network (CNN). The results indicate that our CNN model can blindly and accurately identify the modulation format with classification accuracy up to 99.98% for random channel condition, including the strength of turbulence and signal-to-noise ratio (SNR) of additive Gaussian white noise (AWGN) ranging from 10dB to 30dB. Moreover, the CNN demonstrated robustness against atmospheric optical turbulence and suggested immunity to additive noise. Therefore, the proposed methodology proved to be a viable solution in the application of an FSO communication simulation channel, which can easily deal with the scene of fast modulation format switching and accurate identification to satisfy system requirements. Therefore, we hope this scheme can find a practical implementation in satellite-to-ground optical wireless systems.