概括
这项研究引入了一个卷积神经网络 (CNN) 来预测空气流对轨道角动量 (OAM) 模式在自由空间光通信的影响. 这种方法显著改善了信号接收,并减少了流条件下的交叉通话.
科学领域:
- 光学通信是一种光学通信.
- 大气物理大气物理学
- 机器学习 机器学习
背景情况:
- 大气自由空间链接中的流会扭曲空间光模式.
- 散射破坏了轨道角动量 (OAM) 模式的正交度,导致模态交声.
- 这降低了OAM多重复合自由空间光学 (FSO) 通信的性能.
研究的目的:
- 提出一种基于卷积神经网络 (CNN) 的方法,用于补偿OAM多重化FSO通信中的流.
- 专注于预测大气流本身的OAM特征.
- 在动荡条件下提高FSO链路的整体性能和可靠性.
主要方法:
- 使用操作员方法提取大气流的OAM组件.
- 一个CNN被训练来预测动荡的OAM系数.
- 拟议的网络被用来补偿流效应.
主要成果:
- 基于CNN的方法在微弱到强的流下提高了超过10dB的接收功率.
- 流式OAM模式比Zernike模式更有效地表征流信息.
- 与D/r0=4.4的Zernike模式相比,接收功率提高了4dB.
- 与Zernike模式相比,交叉声量减少了10dB.
结论:
- 拟议的基于CNN的动荡OAM方法有效地弥补了FSO链路中的大气动荡.
- 这种方法在接收功率和交叉通话减少方面,比传统的Zernike模式补偿提供了更高的性能.
- 流式OAM模式为通信系统优化提供了更有效的流表示.
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