在概率信息的帮助下,知识的蒸使SNRL和SNRNL对WDM系统的有效联合监测成为可能
Optics express
|January 29, 2025
概括
本研究引入了一种新的概率信息辅助知识蒸 (PIAKD) 方案,用于在波长分割复杂化 (WDM) 系统中联合监测线性信号对噪声比 (SNRL) 和非线性信号对噪声比 (SNRNL),从而显著减少监测错误.
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:
- 波长分割多重复合 (WDM) 系统面临信号质量退化由于线性和非线性噪声.
- 有效的联合监测对于防止噪声积累导致性能下降至关重要.
研究的目的:
- 为WDM系统中线性信号噪声比率 (SNRL) 和非线性信号噪声比率 (SNRNL) 提出一个智能联合监控方案.
- 通过结合概率信息来解决多任务回归模型中从教师转移到学生知识的挑战.
主要方法:
- 开发了一个概率信息辅助知识蒸 (PIAKD) 计划.
- 将概率信息集成到损失函数中,以增强学习.
- 使用WDM模拟和实验系统验证该方案,每通道符号速率为28 GBaud.
主要成果:
- 在模拟中,PIAKD将SNRL和SNRNL联合监测的总平均绝对误差 (MAE) 降低了32%和34%.
- 与标准知识蒸 (KD) 相比,PIAKD分别为SNRL和SNRNL实现了16%和11%的MAE减少.
- 实验结果显示,SNRL和SNRNL的MAE减少了17%和18%,计算复杂性明显降低 (3.30M FLOP,0.0015M Params).
结论:
- 拟议的PIAKD方案有效地实现了在WDM系统中SNRL和SNRNL的智能联合监控.
- 与现有方法相比,PIAKD在减少错误和计算效率方面表现出卓越的性能.
- 该方法为在先进的光通信系统中保持高信号质量提供了有希望的解决方案.
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