在DP-64QAM尼奎斯特-WDM连贯光通信系统中进行非线性损伤的可解释和轻量级的时间神经网络等效器
Optics express
|November 11, 2025
概括
一个新的神经网络使用扰动理论在光纤系统中实现高效的非线性等分. 这种方法减少了复杂性和延迟,改善了下一代网络的信号质量.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 频道非线性是高速光纤系统的主要性能瓶.
- 传统的非线性等分器存在高度的计算复杂性和有限的实时能力.
- 现有的机器学习方法面临着模型复杂性,推理延迟和可解释性方面的挑战.
研究的目的:
- 为光纤系统提出一种新的,低复杂度的,可解释的非线性均等神经网络.
- 解决传统和当前基于机器学习的等级化技术的局限性.
- 为了提高实时性能和物理解释性在非线性均等化.
主要方法:
- 引入了一个基于扰动的非线性均等神经网络.
- 第一阶扰动理论用于主要组件特征映射.
- 一个封闭形式的连续时间 (CFC) 神经网络用于低复杂度的信号均等.
主要成果:
- 提出的方法在最佳发射光学功率 (LOP) 时提高了1.2dB的Q因子,在测量LOP范围内提高了4.7dB.
- 与数字反向传播 (DBP) 相比,它将LOP范围扩大1dBm,与沃尔特拉非线性均衡 (VLNE) 相比,它将LOP范围扩大4dBm.
- 与长期短期记忆 (LSTM) 神经网络相比,时间复杂性降低了高达79.4%.
结论:
- 这种基于扰动的新型神经网络为光通信中的非线性障碍提供了有效和可解释的解决方案.
- 该方法适用于动态实时传输系统,表现出高效率和轻量化设计.
- 这项工作通过结合扰动理论和自适应机器学习,为高容量,低延迟的光学网络铺平了道路.
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