基于Volterra系列传输函数的光纤WDM传输的多输入神经通道波形模型
Optics express
|September 23, 2025
概括
一个新的深度学习模型,在频域 (NN-VS) 中的神经网络参数化,准确地模拟波长分割多重复合 (WDM) 系统的光纤通道. 它在不同的参数上提供了强大的概括,优于传统方法.
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:
- 精确的光纤通道建模对于波长分割复杂化 (WDM) 系统至关重要.
- 传统的分阶段里叶法 (SSFM) 在计算上是低效的.
- 由于对各种系统参数的再培训要求,现有的深度学习模型缺乏灵活性.
研究的目的:
- 为WDM通道建模开发一个灵活而准确的深度学习模型.
- 克服当前深度学习方法在处理各种系统参数方面的局限性.
- 提高光学通道模型的概括能力.
主要方法:
- 基于物理的沃尔特拉系列转移函数算法.
- 在频率域 (NN-VS) 中的神经网络参数化.
- 通过对40通道和5通道WDM系统的模拟进行评估.
主要成果:
- NN-VS 实现了高精度,平均 Q-因子误差低于 0.15 dB.
- 在不同的波德速率,分散率和非线性系数中展示了强大的概括性.
- 与SSFM相比,显示出更高的计算效率,使用<2%的真实乘法并实现毫秒级GPU运行时间.
结论:
- 在WDM系统中,NN-VS为光纤通道建模提供了高度准确和灵活的解决方案.
- 该模型有效地处理多参数输入,减少重新培训的需要.
- 对SSFM来说,NN-VS是一个计算效率高的替代方案,用于先进的光通信网络设计.
相关概念视频
Transmission-Line Differential Equations
967
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
967
Traveling Waves: Lossless Lines
466
The provided content explores the behavior of traveling waves on single-phase lossless transmission lines. It begins with a single-phase two-wire lossless transmission line of length Δx, characterized by a loop inductance LH/m and a line-to-line capacitance C F/m. These parameters result in a series inductance LΔx and a shunt capacitance CΔx.
466
Transmission Line Design Considerations
601
Aluminum has become the material of choice for overhead transmission lines, surpassing copper due to its abundance and cost-effectiveness. The most prevalent type is the aluminum conductor, steel-reinforced (ACSR), which combines aluminum strands around a steel core. Other variants include all-aluminum conductors (AAC), all-aluminum alloy conductors (AAAC), aluminum conductor alloy-reinforced (ACAR), and aluminum-clad steel conductors. Advanced designs, such as aluminum conductors with steel...
601
State Space to Transfer Function
560
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
560
Propagation Speed of Electromagnetic Waves
4.6K
Electromagnetic waves are consistent with Ampere's law. Assuming there is no conduction current Ampere's law is given as:
4.6K
Transfer Function to State Space
765
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
In an RLC...
765


