Orbital angular momentums (OAMs) greatly enhance the channel capacity in free-space optical communication. However, demodulation of superposed OAM to recognize them separately is always difficult, especially upon multiplexing more OAMs. In this work, we report a directly recognition of multiplexed fractional OAM modes, without separating them, at a resolution of 0.1 with high accuracy, using a multi-task deep learning (MTDL) model, which has not been reported before. Namely, two-mode, four-mode, and eight-mode superposed OAM beams, experimentally generated with a hologram carrying both phase and amplitude information, are well recognized by the suitable MTDL model. Two applications in information transmission are presented: the first is for 256-ary OAM shift keying via multiplexed fractional OAMs; the second is for OAM division multiplexed information transmission in an eightfold speed. The encouraging results will expand the capacity in future free-space optical communication.

轨道角动量（OAM）极大地增强了自由空间光通信中的信道容量。然而，解调叠加的 OAM 以分别识别它们总是很困难，特别是在复用更多 OAM 时。在这项工作中，我们报告了使用多任务深度学习（MTDL）模型以 0.1 的分辨率高精度地直接识别多路复用分数 OAM 模式，而无需将它们分离，这在之前尚未报道过。也就是说，用携带相位和幅度信息的全息图实验生成的二模、四模和八模叠加 OAM 光束可以被合适的 MTDL 模型很好地识别。提出了信息传输中的两个应用：第一个是通过复用分数 OAM 进行 256 进制 OAM 移位键控；第二种是八倍速的OAM分复用信息传输。这一令人鼓舞的结果将扩大未来自由空间光通信的容量。