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学习了沃尔特拉模型,用于波长分割多重系统中的非线性等分
Optics express
|August 13, 2025
概括
三种新的机器学习模型用于多通道数字均等化,有效地减少波长分割多重系统中的光纤损伤. FE L-IVSTF模型为光通信网络提供了最佳的性能和计算效率.
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:
- 光纤诱导的通道间损伤限制了波长分割多重 (WDM) 系统.
- 多通道数字均等对于缓解这些障碍至关重要.
研究的目的:
- 引入和评估三种新的多输入多输出 (MIMO) 学习等效架构.
- 评估它们在缓解WDM系统中道间损害方面的有效性.
主要方法:
- 开发了基于逆伏尔特拉序列传递函数 (IVSTF) 的三个MIMO架构:L-IVSTF (频域),FE L-IVSTF (场增强) 和L-simIVSTF (时间域).
- 利用机器学习优化进行等级.
- 基于性能和计算成本,描述和比较模型.
主要成果:
- 这三种架构都表现出高效的多道均等.
- 9x9 L-simIVSTF 和 FE L-IVSTF 在色差分散补偿上实现了 ~2.2 dB 的平均信号噪声比 (SNR) 改进.
- FE L-IVSTF模型显示了性能和计算负载之间的最佳平衡.
结论:
- 机器学习优化显著提高了多道等级能力.
- FE L-IVSTF架构为光学系统的实际实施提供了一个优越的权衡.
- 这些先进的等分技术对于推进高容量光通信至关重要.
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