基于机器学习驱动的轨道角动量的光学编码模型。
Optical Encoding Model Based on Orbital Angular Momentum Powered by Machine Learning.
机构信息
Escuela Superior Politécnica del Litoral, ESPOL, Departamento de Física, Campus Gustavo Galindo, Km 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil 090150, Ecuador.
Facultad de Ciencias Matemáticas y Físicas, Universidad de Guayaquil, Guayaquil 090514, Ecuador.
出版信息
Sensors (Basel). 2023 Mar 2;23(5):2755. doi: 10.3390/s23052755.
Based on orbital angular momentum (OAM) properties of Laguerre-Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of and indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER =10-9 for 10.2 dB of signal-to-noise ratio in one of the SVM models.
基于拉盖尔-高斯光束 LG(p,ℓ)的轨道角动量 (OAM)特性，设计了一种用于高效数据传输应用的稳健光学编码模型。本文提出了一种基于相干叠加两个携带轨道角动量的拉盖尔-高斯模式的强度分布的光学编码模型，以及一种机器学习检测方法。在编码过程中，根据 和 指数的选择生成用于数据编码的强度分布，而解码过程则使用支持向量机 (SVM)算法完成。测试了两种不同的基于 SVM 算法的解码模型，以验证光学编码模型的稳健性，其中一个 SVM 模型在信噪比为 10.2dB 时达到 BER=10-9。
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