Neural Network Model for OAM Crosstalk due to Turbulence-Induced Tilt and Lateral Displacement

Abstract

Accurately modelling orbital angular momentum (OAM) mode crosstalk in turbulent environments is challenging yet essential for developing free-space optical systems that employ OAM modes for multiplexing or diversity. Turbulence induces tip/tilt aberrations and lateral displacement, which significantly degrade system performance. Existing analytical models describe the transformation from an input Gaussian mode to an output OAM spectrum; however, our feed forward neural network model generalizes this approach by accounting for the effects of these aberrations on arbitrary input OAM modes. We validate the model experimentally by estimating turbulence-induced tilt and lateral displacement using a dual-camera setup and comparing the estimated spectrum with the actual modal decomposition. With a typical root mean square error of less than 11%, our results indicate that the model could serve as a reliable source of meta-information for digital signal processing, soft-decision forward error correction or perhaps dynamic mode hopping in future systems.