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Updated: Feb 1, 2026

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对基于DML的IM/DD系统的几何造型进行可解释模型驱动的端到端深度学习
Optics letters
|January 30, 2026
概括
直接调制激光器 (DML) 遭受非线性扭曲. 一个新的基于模型的框架使用神经网络自编码器来优化几何星座,提高光通信系统中的接收器灵敏度.
科学领域:
- 光学通信是指光学通信.
- 非线性光学是非线性光学.
- 在光子学中的机器学习.
背景情况:
- 直接调制激光器 (DML) 在短距离光学系统中具有成本效益.
- 片状分散相互作用会导致非线性扭曲,限制DML的性能.
- 神经网络 (NN) 自编码器 (AE) 基于几何形状 (GS) 提供星座优化,但需要广泛的训练数据.
研究的目的:
- 开发一个低复杂度的,以模型驱动的框架,用于优化DML系统中的几何星座.
- 为了减轻由声分散相互作用引起的非线性扭曲.
- 将基于物理的可解释性与深度学习相结合,用于自适应式通道建模.
主要方法:
- 提出了一个基于模型的框架,使用复合二阶 (CSO) 扭曲理论来创建替代通道模型.
- 在代用通道上使用基于AE的几何星座优化.
- 通过在10公里的标准单模光纤 (SSMF) 上进行实验传输来验证该方法.
主要成果:
- 实现了声分散相互作用诱导的非线性扭曲的抑制.
- 对于64-QAM信号,显示了1dB的接收器灵敏度改进.
- 证实了基于物理的深度学习方法的有效性.
结论:
- 拟议的低复杂性框架有效地减轻了DML系统中的非线性扭曲.
- 将模型驱动的洞察力与深度学习相结合,为星座优化提供了有效的解决方案.
- 这种方法提高了短距离光通信系统的性能.
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