对于神经形态光子学的二项式训练算法,应用于通道均等化
Applied optics
|September 22, 2025
概括
我们介绍了一个新的光子神经网络,使用双项训练来实现光通道均等. 该系统显著提高光纤传输中的信号质量,展示了强大的性能和通用化能力.
科学领域:
- 光子学是指光子学的使用方法.
- 光学通信是指光学通信.
- 人工智能的人工智能
背景情况:
- 光通信系统在长距离上面临信号退化.
- 频道平衡对于保持信号完整至关重要.
- 现有的等式化方法可能是复杂和计算密集的.
研究的目的:
- 提出和评估一个新的光子时间延迟神经网络,用于通道均等.
- 为了证明这个光学神经网络的二项训练算法的有效性.
- 评估开发系统的性能和通用化能力.
主要方法:
- 使用延迟线,相位和振幅调节器开发光子时间延迟神经网络架构.
- 使用从10 Gb/s光传输系统实验获得的数据进行数值实现和培训.
- 评估比特错误率 (BER) 和眼睛图表在等分后的改进.
主要成果:
- 在10Gb/s开关关键 (OOK) 信号的BER和眼睛图显著改进,超过100公里的单模式光纤.
- 光子神经网络成功地恢复了高度降解的信号,打开了闭眼图.
- 93.3%的传输比特序列的性能低于前置错误校正 (FEC) 极限,表明强烈的泛化.
结论:
- 拟议的光子时间延迟神经网络与双项训练为光通道均等提供了有效的解决方案.
- 离散的,硬件友好的优化适用于光子集成电路.
- 该系统表现出卓越的性能和通用性,适用于现实世界的光传输挑战.
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