有助于学习的数据传输,通过商用多模式光纤传输多达32个多重轨道角动量频道
Optics letters
|April 15, 2024
概括
本研究介绍了一种机器学习系统,用于使用轨道角动量 (OAM) 模式的高容量光通信. 它准确地对多重的OAM通道进行排序,即使在分散环境中也能够实现可靠的数据传输.
科学领域:
- 光学通信是指光学通信.
- 机器学习应用 机器学习应用
- 光子学是指光子学的使用方法.
背景情况:
- 高容量光通信依赖于多重化轨道角动量 (OAM) 模式.
- 标准纤维中的强模式合会产生类似的OAM模式模式,从而阻碍了解复.
- 散射环境进一步使OAM信号恢复复杂化.
研究的目的:
- 开发一个机器学习支持的系统,用于解复杂化大量的OAM道.
- 为了克服模式合和光纤中的散射所带来的挑战.
- 为了使高精度的数据传输使用分数OAM复杂化.
主要方法:
- 提出了一种基于机器学习的分数OAM复杂数据传输系统.
- 该系统在有限的样本上进行了训练,以预测未见的OAM超级州.
- 商业多模式光纤被用来传播多达32个多重的OAM通道.
主要成果:
- 在从32个多重化OAM通道中排序高度分散的数据时实现了>99.92%的准确性.
- 证明了对未见的OAM超级状态的精确预测,减少了数据集大小.
- 成功传输了灰色和彩色图像,错误率低于0.26%.
结论:
- 拟议的系统为高容量的OAM光通信提供了一个有希望的解决方案.
- 机器学习有效地解决了OAM在散射环境中的解复杂化挑战.
- 这种方法提升了强大的光通信系统的潜力.
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