科尔莫戈罗夫-阿诺德网络,用于在短距离的IM/DD系统中实现高效的均等化
Optics express
|September 23, 2025
概括
本研究介绍了新的Kolmogorov-Arnold基于网络的等分器 (KAN-E和FKAN-E),用于在短距离光通信系统中高效的信号等分. 这些网络显著降低了计算复杂性,同时保持了卓越的比特错误率性能.
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:
- 神经网络用于短距离强度调制直接检测 (IM/DD) 系统中的非线性等分.
- 现有的神经网络等分器的高计算复杂性限制了实时应用.
- 在高速光学互连中需要高效的均等化技术.
研究的目的:
- 提出和评估一种新的Kolmogorov-Arnold基于网络的等分器 (KAN-E),用于IM/DD系统中的高效等分.
- 通过开发基于快速KAN的等分器 (FKAN-E) 来进一步优化等分效率.
- 为了证明KAN-E和FKAN-E在C频直接调制激光 (DML) 基于光学链路的性能.
主要方法:
- 使用基于C频段DML的20公里和10公里IM/DD光学链路进行实验演示.
- 在不同数据速率下传输脉冲振幅调制 (PAM) - 6和PAM - 8信号.
- 实施和比较KAN-E和FKAN-E与Feedforward神经网络等效器 (FNN-E) 和卷积神经网络等效器 (CNN-E).
主要成果:
- 与FNN-E和CNN-E相比,KAN-E实现了高达65.9%的参数减少,与FNN-E和CNN-E相比,BER性能优越,支持>40 Gbaud PAM-6/8.
- 使用高斯辐射基函数的FKAN-E消除了复杂的插值,减少了计算开销,同时保持了与KAN-E相比的BER.
- 与KAN-E相比,FKAN-E显著减少了训练和推断时间:分别减少了19.9%和58.0% (20公里PAM-6),以及27.0%和48.4% (10公里PAM-8).
结论:
- 拟议的KAN-E和FKAN-E在短距离IM/DD系统中提供高性能和低复杂性等级.
- 与KAN-E相比,FKAN-E可以大幅提高计算效率,而不会影响BER性能.
- 这些基于KAN/FKAN的均等化方案是未来在光学互连中IM/DD接收器实现的有希望的候选者.
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