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利用分数阶涡旋光束（Fractional vortex beams, FVBs）作为信息载体可显著提高通信系统容量，但由于相邻分数阶轨道角动量（ Fractional Orbital AngularMomentum, FOAM） 模态之间的间隔差异较小，使得 FVBs 极易受到大气湍流影响，因此精确测量失真的 FOAM 模态对实际基于 FVBs 的通信系统而言至关重要。本文提出了一种基于卷积神经网络-Transformer 混合架构的双通道深度学习模型，通过学习并融合 FVBs 光强分布与其衍射图样的互补特征信息，实现对大气湍流环境下 FOAM 模态的有效识别。 结果表明，在 1000m 传输距离之内，本文构建模型在弱、中湍流强度下识别 101 个 FOAM 模态的准确率可达 100%，强湍流时也能达到 98.12%， 并且在未知湍流强度下也表现出良好的泛化能力，为准确识别 FOAM 模态提供了一种新方法。Utilizing fractional vortex beams (FVBs) as information carriers can significantly enhance the capacity of communication systems. However, the small gap difference among adjacent fractional orbital angular momentum (FOAM) modes makes FVBs highly sensitive to atmospheric turbulence. Therefore, precise measurement of distorted FOAM modes is crucial for practical FVBs-based communication systems. To fully utilize the beam intensity information and the triangular diffraction pattern information, we propose a dual-channel deep learning model with a hybrid architecture combining convolutional neural network (CNN) and Vision Transformer(ViT). The beam intensity information is extracted using CNN, while the diffraction pattern information is extracted using ViT. Then, by fusing the complementary feature information from the intensity distribution of FVBs and their triangular diffraction patterns, this model enables effective identification of FOAM modes. The results show that the proposed model only requires a relatively small number of samples to reach the convergence value, namely 100 sets of data under weak turbulence and 400 sets of data under strong turbulence. Moreover, within a transmission distance of 1000 m, the proposed model can identify 101 FOAM modes with a mode spacing of 0.1 with an accuracy of 100% under weak and moderate turbulences, and maintains 98.12% accuracy under strong turbulence. Furthermore, the model can expand the detection range of turbulence intensity with only a minimal loss in accuracy, exhibiting strong generalization ability under unknown atmospheric turbulence strengths, thus providing a novel approach for accurately identifying FOAM modes.
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Keywords:
- Fractional Orbital Angular Momentum /
- Deep Learning /
- Triangular Diffraction /
- Atmospheric Turbulence
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