在数字子载波复杂化系统中用于光通道非线性补偿的神经网络架构
Optics express
|September 15, 2023
概括
人工神经网络 (ANN) 可以模拟和补偿数字子载波复杂化 (DSCM) 系统中的光纤非线性干扰. 优化的ANN结构对于未来的连贯光学收发器至关重要.
科学领域:
- 光学通信是指光学通信的应用.
- 电信领域的人工智能
背景情况:
- 数字子载波复杂化 (DSCM) 系统面临子载波内部和子载波之间纤维非线性干扰.
- 准确的建模和对这些非线性进行补偿对于系统性能至关重要.
研究的目的:
- 提出和评估各种人工神经网络 (ANN) 结构,用于模拟和补偿 DSCM 系统中纤维非线性干扰.
- 探索ANN架构对非线性通道均等化性能-复杂性权衡的影响.
主要方法:
- 使用不同的ANN核心,包括卷积神经网络 (CNN) 和长短期记忆 (LSTM) 层.
- 开发基于完全连接的网络的光纤非线性补偿,逐步升级到模块化ANN结构.
- 使用光纤非线性扰动分析来指导设计改进.
主要成果:
- 证明了ANNs在模拟和补偿DSCM系统中纤维非线性干扰方面的有效性.
- 与完全连接的网络相比,模块化ANN结构展示了性能改进.
- 确定了ANN宏观结构设计对于实际解决方案的关键作用.
结论:
- 人工神经网络为DSCM系统中的非线性干扰补偿提供了一种可行的方法.
- 无线网络宏观结构的设计显著影响非线性均等器的效率和有效性.
- 基于ANN的优化非线性等分器是推动未来连贯光学收发器技术发展的关键.
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