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

Mitchell A. Cox; Steven G. Makoni; Ling Cheng

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arXiv · 2509.03603

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

Mitchell A. Cox·Steven G. Makoni·Ling Cheng

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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.

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Mitchell A. Cox, Steven G. Makoni, Ling Cheng. 2025-09-03. Neural Network Model for OAM Crosstalk due to Turbulence-Induced Tilt and Lateral Displacement. https://arxiv.org/abs/2509.03603

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