通过深度学习方法识别几模式光纤中存在的混合模式的轨道-角度-动量频谱
Optics express
|September 15, 2023
概括
本研究介绍了一种使用卷积神经网络 (CNN) 的深度学习方法,以精确地重建几模纤维中的轨道角动量 (OAM) 频谱. 这种方法从强度模式准确地识别出所有OAM模式,甚至是复杂的混合模式.
科学领域:
- 光学通信是指光学通信的应用.
- 信号处理 信号处理
- 人工智能的人工智能是人工智能.
背景情况:
- 轨道角动量 (OAM) 复杂化是增加光纤通信容量的有希望的技术.
- 准确的OAM光谱重建对于有效的信号解码和系统性能至关重要.
- 现有的OAM频谱重建方法可能很复杂,并且可能与混合OAM模式发生冲突.
研究的目的:
- 开发和验证一种精确和快速的方法来重建混合OAM模式的OAM频谱.
- 为了证明OAM模式识别在少数模式纤维中的深度学习方法的有效性.
- 建立一种新的技术来识别光纤中所有同时存在的OAM模式.
主要方法:
- 卷积神经网络 (CNN) 与残余块和回归相结合的理论和实验研究.
- 利用深度学习来分析混合OAM模式的强度分布.
- 在四模纤维 (4MF) 和六模纤维 (6MF) 中模拟和测试该方法.
主要成果:
- 提出的基于CNN的方法准确地重建了OAM光谱,用于具有相同或不同的亚齐图斯和辐射指数的混合OAM模式.
- 模拟测试显示4MF的平均误差低于0.003,6MF的平均误差低于0.008.
- 这代表了第一个使用深度学习方法在4MF和6MF中精确和快速识别所有潜在存在的OAM模式的演示.
结论:
- 深度学习,特别是剩余块的CNN,为准确和快速的OAM频谱重建提供了一个强大的工具.
- 该方法成功地识别了混合状态中的所有OAM模式,克服了以前技术的局限性.
- 这一突破使得利用OAM复杂化实现更高效,更强大的光通信系统.
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