概括
使用卷积神经网络 (CNN) 和双向长短期记忆 (LSTM) 的新型神经网络方法显著改善了安全的光学混乱通信. 这种方法在长距离光纤传输后降低了高阶信号的比特误差率 (BER).
科学领域:
- 光电学是指光电子产品.
- 安全的通信安全的通信.
- 在光子学中的人工智能.
背景情况:
- 光学混沌通信提供了与现有的光纤相容的高速,安全的数据传输.
- 光纤传输障碍会降低混乱的同步,影响信息恢复,特别是对于高阶调制信号.
研究的目的:
- 研究混合卷积神经网络 (CNN) 和双向长短期记忆 (LSTM) 模型在光学混乱通信中减轻解密位误差率 (BER) 的有效性.
- 分析神经网络参数和混乱同步系数对系统性能的影响.
主要方法:
- 实现用于信号解密的CNN-LSTM神经网络架构.
- 半导体激光器的同步由一个共同的信号诱导.
- 在模拟光纤传输损伤下对CNN-LSTM模型性能进行实验验证.
主要成果:
- 在CNN-LSTM模型成功地降低了16ary正方形幅度调制 (16QAM) 信号在100公里光纤中传输的BER.
- 传输后的BER从最初的3.05 × 10-2降低到低于软决策前置错误校正 (SD-FEC) 值的2.0 × 10-2的水平.
结论:
- 拟议的CNN-LSTM方法有效地提高了光学混乱通信系统的稳定性和可靠性,以应对光纤传输损伤.
- 这种人工智能驱动的解密方法在安全的光通信系统中,在高阶调制信号中改善恢复信息的质量方面显示出显著的希望.
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