概括
这项研究引入了一个罗网络来纠正扭曲的束,并确定在水下无线光通信 (UWOC) 中的轨道角动量 (OAM) 模式. 该方法有效地减轻了海洋流 (OT) 的影响,提高了UWOC系统的性能.
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:
- 束可以提高水下无线光通信 (UWOC) 的通道容量.
- 海洋流 (OT) 严重扭曲了束,降低了UWOC质量.
- 准确识别轨道角动量 (OAM) 模式和扭曲校正对于强大的UWOC至关重要.
研究的目的:
- 为UWOC系统开发联合扭曲校正和OAM模式识别方法.
- 通过实验验证使用语网络 (SN) 验证拟议的方法.
- 为了在有限的数据下实现准确的OAM模式识别和Zernike系数预测.
主要方法:
- 采用罗网络 (SN) 架构从相屏幕和强度模式中进行特征提取和融合.
- 在SN中集成了一个分类网络,用于同时OAM模式识别和Zernike多项式系数预测.
- 该方法旨在在有限数量的培训样本中有效运行.
主要成果:
- 该SN准确地确定了四种OAM模式,并预测了四种海洋动荡 (OT) 级别的泽尼克系数.
- 使用预测系数重建的相屏幕使得扭曲的旋转束能够得到高质量的校正.
- 即使采用有限的样本数据,SN也表现出强大的概括性能.
结论:
- 提出的基于SN的方法为纠正UWOC中束扭曲提供了有效的解决方案.
- 这种方法可以在具有挑战性的水下环境中可靠地识别OAM模式并减轻流.
- 该研究为改善UWOC系统的性能和可靠性提供了一条新的途径.
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