Title: Neural Network Model for OAM Crosstalk due to Turbulence-Induced Tilt and Lateral Displacement Show Chinese title

Title: 湍流引起的倾斜和横向位移导致的OAM串扰的神经网络模型

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. Show Chinese abstract

Abstract: 准确建模湍流环境中轨道角动量（OAM）模式串扰是具有挑战性的，但对于开发使用OAM模式进行复用或分集的自由空间光系统至关重要。湍流会引起倾斜/倾斜像差和横向位移，这会显著降低系统性能。现有的分析模型描述了从输入高斯模式到输出OAM频谱的转换；然而，我们的前馈神经网络模型通过考虑这些像差对任意输入OAM模式的影响，推广了这一方法。我们通过使用双相机设置估计湍流引起的倾斜和横向位移，并将估计的频谱与实际模式分解进行比较，从而对模型进行了实验验证。结果表明，该模型的典型均方根误差小于11%，可以作为未来系统中数字信号处理、软判决前向纠错或可能的动态模式跳变的可靠元信息来源。