Abstract
The automatic modulation recognition (AMR) of optical signals in non-cooperative conditions has recently become one of the most important research projects. The feasibility and effectiveness of using deep learning techniques for automatic modulation format recognition of received wireless optical communication signals is investigated in this framework. Convolutional neural network (CNN) architecture has been developed and refined in recent work to attain performance that outperforms expert-based approaches. An AMR technique based on fan-beam (FB) projection was proposed in this study. The constellation diagram is created by preprocessing the received signal. The constellation diagram is then projected in the FB domain. The FB is used as a tool for extracting features. The (CNN) uses this projected constellation diagram as input to identify different types of modulation formats. For determining the modulation format of an optical signal, three types of classifiers (Alexnet, VGG16, and VGG19) are investigated. For our investigations, we looked at the constellation diagrams of eight common signals, including (2/4/8/16- phase-shift keying (PSK) and 8/16/32/64- quadrature amplitude modulation (QAM). Typical classification accuracy values versus optical signal-to-noise ratio (OSNR) are calculated for evaluation over an OSNR range of (5-30 dB). A study of the effect of increasing the number of samples on the accuracy of the classifiers is also presented for each modulation format. Finally, the phase noise resistance of the (FB) projected diagram is investigated. When compared to traditional modulation format recognition algorithms, simulation results suggest that the proposed algorithm presented in this paper has a greater accuracy rate for signal modulation recognition at low OSNR.