概括
这项研究引入了一个稳定的神经网络,OATNN,用于识别轨道角动量 (OAM) 模式和大气流. 它实现了高精度,提高了自由空间光通信质量.
科学领域:
- 光学通信是指光学通信.
- 大气物理学 大气物理学
- 机器学习 机器学习
背景情况:
- 轨道角动量 (OAM) 束为光通信提供了高容量.
- 大气流会导致OAM光束的扭曲,导致信号干扰和通信质量的降低.
- 稳定的OAM模式识别对于可靠的自由空间光通信至关重要.
研究的目的:
- 开发一种稳定高效的方法,同时识别OAM模式和大气流强度.
- 增强自由空间光通信系统对抗大气干扰的稳定性.
主要方法:
- 确定了连续动态系统和稳定网络单元 (RUEM) 之间的等价性.
- 提出了一种多任务神经网络,OATNN,结合RUEM用于同时OAM模式和流强度识别.
- 进行数值实验以验证模型的稳定性和准确性.
主要成果:
- 在识别四个流强度级别时,OATNN实现了99.37%的准确性.
- 该网络在识别十种OAM模式时显示了99.05%的准确性.
- 在2000米通道传输场景中评估了性能.
结论:
- 拟议的OATNN模型与RUEM嵌入,为OAM模式和流强度识别提供了稳定而准确的解决方案.
- 这种方法显著提高了在动荡的大气道中自由空间光通信的质量和可靠性.
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