概括
一个新的深度学习算法有效地对抗无线光通信中的大气动荡. 这种时空融合方法显著降低了比特误差率 (BER),提高了信号可靠性.
科学领域:
- 光学通信系统 光学通信系统
- 大气流的建模大气流.
- 信号处理 信号处理
背景情况:
- 大气流会导致无线光学系统的信号退化 (闪,多路径干扰),增加比特错误率 (BER).
- 现有的通道均方法在缓解这些影响方面面临性能限制.
研究的目的:
- 为大气流道提出一种新的深度学习道均等算法.
- 解决传统均等化技术的性能瓶问题.
- 为了有效地减轻大气流引起的色效应.
主要方法:
- 使用测量光强度数据开发了大气流道模型,结合了闪光和多路径效应.
- 提出了一个空间时空特征融合深度学习算法用于通道均等.
- 在各种流分布下使用16QAM和DCO-OFDM16QAM调制评估了算法的性能 (日志正常,马马,指数维布尔).
主要成果:
- 与卷积神经网络方法相比,时空融合算法显著降低了BER.
- 在日志正常分布下,BER从10^-2下降到10^-5.
- 在Gamma-Gamma和指数维布尔分布下,BER分别从10^-2提高到10^-6和10^-5.
- 在低信号噪声比条件下,该算法实现了10^-5到10^-6的BER.
结论:
- 拟议的深度学习算法有效地消除了大气流道的色效应.
- 时空融合方法显示出显著的效率和在无线光通信系统中的实际应用潜力.
- 该算法比传统方法提供了显著的性能改进,特别是在具有挑战性的通道条件下.
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