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基于神经网络的非线性等分器使用视焦优化为PCS 128 QAM 1 Tbit/s光学传输
Optics letters
|August 15, 2025
概括
我们推出了新的视力焦点损失 (SFL) 和SFL-NNLE,以提高高速光学系统的训练效率. 这种方法减轻了过和消失的梯度,提高了复杂变速箱的性能.
科学领域:
- 光学通信是指光学通信的应用.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 高速连贯光学系统面临非线性损害的挑战.
- 对于非线性均等器 (NNLEs) 的传统机器学习方法可能会遭受培训效率低下的影响.
- 过度装配和消失梯度是现有损失函数的常见问题.
研究的目的:
- 提出一种新的视力焦点损失 (SFL) 和基于SFL优化的神经网络非线性均等器 (SFL-NNLE).
- 提高NNLE在高速光学系统方面的培训效率.
- 为了减轻常规损失函数固有的过和消失梯度问题.
主要方法:
- 开发了一种新的视力焦点损失 (SFL) 优化策略.
- 实施了一个SFL-NNLE,扩展传统的机器学习方法.
- 利用空间信息,专注于边界近接的硬样本,以动态优先考虑错误分类.
主要成果:
- 实验证明了SFL-NNLE对非线性损伤的有效性.
- 在118-GBaud DP-PCS 128 QAM BTB传输中,在24%的SD-FEC值时获得了1.45 dB的OSNR增益.
- 在背对背和150公里SSMF传输中实现了超过1 Tbps的净位率.
结论:
- 拟议的SFL-NNLE有效地减轻了高速光学系统中的非线性损伤.
- 优化SFL提高了训练效率,并解决了传统损失函数的局限性.
- 该SFL-NNLE能够实现超高的净比特率超过1 Tbps.
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