概括
我们引入了一个新的端到端学习方案,使用时间频率脱网络进行概率塑造和空心光纤系统的预等值,提高OSNR高达1.6dB.
科学领域:
- 光学通信是指光学通信.
- 在光子学中的机器学习.
- 光纤传输的光纤传输
背景情况:
- 空心纤维 (HCF) 为高容量的波长分割多重复合 (WDM) 系统提供了潜力.
- 在HCF中的信号损害,包括线性和非线性扭曲,对系统性能构成挑战.
- 联合概率塑造 (PS) 和预等值对于减轻这些损害至关重要.
研究的目的:
- 提出一个新的端到端 (E2E) 学习方案,用于在基于HCF的WDM系统中联合PS和预等级.
- 开发一个时间频率脱网络 (TFDnet) 模拟器,用于建模HCF通道损伤.
- 通过优化信号处理,提高基于HCF的WDM系统的性能.
主要方法:
- 一个时间频率脱网络 (TFDnet) 被开发出来,通过分离信号损害来建模HCF传输通道.
- 使用TFDnet模拟器实施了一个E2E学习策略,用于联合PS和预等级.
- 实验验证是在基于HCF的30通道,10公里WDM系统上进行的.
主要成果:
- 基于TFDnet的联合PS和预等级方案实现了优越的比特错误率 (BER) 性能.
- 与基于CGAN和传统方法相比,观察到光学信号与噪声比 (OSNR) 的改善分别为1.0dB和1.6dB.
- 该计划达到20%的硬决策前期错误纠正 (HD-FEC) 门.
结论:
- 拟议的基于TFDnet的E2E学习方案有效地弥补了基于HCF的WDM系统中的信号损伤.
- 这种方法证明了OSNR的显著改进,提高了系统容量.
- 对于未来的超高容量光通信系统,TFDnet战略显示出前景.
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