基于写入少模光纤的长周期光纤光栅的可调模式转换器实验数据集:热、波长和偏振变化的影响

Data Pub Date : 2023-12-31 DOI:10.3390/data9010010
Juan Soto-Perdomo, E. Reyes-Vera, J. Montoya-Cardona, Pedro Torres
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引用次数: 0

摘要

模式划分多路复用(MDM)是目前光通信领域最具吸引力的多路复用技术之一,因为它可以增加可用于数据传输的信道数量。光模态转换器是这项技术中使用的主要设备之一。因此,对这些设备进行表征和改进是当前人们非常关心的问题。在这项工作中,我们展示了一个模态转换器的 49736 幅近场强度图像数据集,该转换器基于写在少模光纤(FMF)上的长周期光纤光栅(LPFG)。当设备从 LP01 模式转换到 LP11 模式时,在不同波长、偏振和温度条件下进行了实验表征。结果表明,模态转换器可以通过调整这些参数进行调整,而且在对其性能有很大影响的特定情况下,其运行是最佳的。此外,数据库的潜在应用也在这项工作中得到了验证。基于粒子群算法(PSO)的模态分解技术被用作从数据集中收集的空间分布中确定模态权重和相对相位最有效组合的工具。所提议的数据集可为研究与 MDM 技术相关的图像分割、检测和分类问题的研究人员带来新的机遇。此外,我们还采用了新颖的人工智能技术,有助于为这类设备找到最佳运行条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experimental Dataset of Tunable Mode Converter Based on Long-Period Fiber Gratings Written in Few-Mode Fiber: Impacts of Thermal, Wavelength, and Polarization Variations
Mode division multiplexing (MDM) is currently one of the most attractive multiplexing techniques in optical communications, as it allows for an increase in the number of channels available for data transmission. Optical modal converters are one of the main devices used in this technique. Therefore, the characterization and improvement of these devices are of great current interest. In this work, we present a dataset of 49,736 near-field intensity images of a modal converter based on a long-period fiber grating (LPFG) written on a few-mode fiber (FMF). This characterization was performed experimentally at various wavelengths, polarizations, and temperature conditions when the device converted from LP01 mode to LP11 mode. The results show that the modal converter can be tuned by adjusting these parameters, and that its operation is optimal under specific circumstances which have a great impact on its performance. Additionally, the potential application of the database is validated in this work. A modal decomposition technique based on the particle swarm algorithm (PSO) was employed as a tool for determining the most effective combinations of modal weights and relative phases from the spatial distributions collected in the dataset. The proposed dataset can open up new opportunities for researchers working on image segmentation, detection, and classification problems related to MDM technology. In addition, we implement novel artificial intelligence techniques that can help in finding the optimal operating conditions for this type of device.
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