通过神经网络辅助的接收器进行单体通信,而单体相互作用会损害单体通信
Optics express
|January 5, 2024
概括
神经网络通过检测单元和抑制非线性损伤来增强单元通信. 一个分类器设计显示了符号检测的卓越性能,提高了可实现的信息速率.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 索利顿通信系统提供高速数据传输,但存在非线性损害.
- 这些障碍,包括单子间相互作用和时间动,降低了信号质量,限制了可实现的信息速率.
- 目前基于模型的检测方案难以完全减轻这些复杂的非线性效应.
研究的目的:
- 提出和评估基于神经网络的新方法,用于在振幅调制系统中检测单子.
- 为了抑制放大器噪声以外的非线性损伤,从而提高可实现的信息速率.
- 将基于学习的方案与传统基于模型的方法的性能进行比较.
主要方法:
- 数字模拟被用来全面研究非线性障碍,如单体间相互作用和戈登-豪斯效应.
- 使用模拟数据开发了两个神经网络架构,即回归网络和分类器,用于单元检测.
- 对于拟议的神经网络方案,估计了可实现的信息速率,并与非线性里埃转换和连续频谱辅助固有值估计进行了基准测试.
主要成果:
- 两种拟议的基于学习的单独子检测设计都显示出与基准方案相比,显著的性能提升.
- 从单独的相互作用中利用通道内存,在可实现的信息速率上产生了额外的改进.
- 基于分类器的神经网络设计显示了对相互作用障碍的卓越适应性,优于回归网络.
结论:
- 基于神经网络的方法显著提高了振幅调制单元通信系统中可实现的信息速率.
- 分类器设计对于符号检测任务特别有效,因为它可以适应非线性损害.
- 利用通道内存对于最大限度地提高未来单一通信系统的性能来说至关重要.
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