Deep learning assisted InAs/InP quantum-dash laser structured light modes detection under foggy channel.

This paper demonstrates L-band quantum-dash laser (QDL) based orbital angular momentum (OAM) structured light in a free space optics communication (FSO) system. A 4-ary OAM-shift-keying pattern coding communication system, based on Laguerre Gaussian (LG) and superposition LG (MuxLG) mode families, has been investigated under a foggy FSO channel. In addition, joint mode identification and channel condition estimation have been developed at the receiver side using advanced deep learning (DL) methods. We utilize and compare the performance of the convolutional neural networks (CNN) and UNET algorithms. An experimental setup has been conducted using an in-house controlled foggy chamber which allows an FSO transmission of 3-m length. Furthermore, we propose a data balancing approach to the experimental dataset by data augmentation. Visibility prediction results have shown a measured root mean square error of 17(18) m and 10(10) m for 4-ary LG(MuXLG) using CNN and UNET models, respectively. Moreover, the DL models provide an average mode classification accuracy of 94% under various channel visibility conditions. • OAM mode patterns was generated using QDL source and identified under foggy channel conditions. • Eight modes have been formed from the LG mode family. • A controlled chamber environment to emulate the outdoor fog condition was considered. • CNN and UNET DL methods were used as classifiers and regressors for mode identification and channel condition prediction. • Visibility parameter can be estimated with 17 and 10 m root mean square error (RMSE) using CNN and UNET techniques. [ABSTRACT FROM AUTHOR]