基于频域特征解网络模拟器的端到端学习策略,并为300Gbit/s的OAM模式划分多重化传输进行了联合概率塑造和均等化
Optics express
|June 11, 2024
概括
这项研究介绍了一个新的AI模拟器用于轨道角动量模式分割复杂化 (OAM-MDM) 系统. FDFDnet模拟器显著提高了建模精度和接收器灵敏度,从而实现了更高的数据传输能力.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 模式合和非线性损伤是OAM-MDM IM/DD系统中持续存在的挑战.
- 准确的系统建模对于减轻这些损害和提高传输性能至关重要.
研究的目的:
- 提出一个端到端的学习策略,使用一个新的FDFDnet模拟器用于OAM-MDM IM/DD系统.
- 通过共同的概率形状和等级来弥补信号损伤.
- 为了提高非线性系统建模的准确性.
主要方法:
- 开发一个频域特征脱网络 (FDFDnet) 模拟器.
- 实施一个端到端的学习策略,将FDFDnet与联合概率塑造和均等化集成在一起.
- 使用300 Gbit/s CAP-32信号在10公里的环芯光纤上进行实验验证,具有三种OAM模式.
主要成果:
- 与传统的CGAN相比,FDFDnet模拟器表现出更高的建模准确性,在三个OAM模式中得到了30.8%,26.3%和31%的改进.
- 拟议的E2E学习策略实现了增强的接收器灵敏度,超过了CGAN模拟器的2.2-3dBm和真实通道的5.1-5.5dBm.
- 实验结果证实了基于FDFDnet的方法在补偿信号损害方面的有效性.
结论:
- FDFDnet模拟器为OAM-MDM系统提供了准确的非线性建模.
- 提出的E2E学习策略有效地弥补信号损害,提高接收器的灵敏度.
- 这种方法显示了实现超高容量数据中心互连的巨大潜力.
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