Information transmission through parallel multi-task-based recognition of high-resolution multiplexed orbital angular momentum

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.