概括
本研究介绍了一种使用变压器-LSTM神经网络进行安全光纤无线通信的新型音调保留 (TR) 加密方案. 该方法增强了安全性并减少了信号扭曲,实现了高预测准确性和低位错误率.
科学领域:
- 信息安全 信息安全
- 电信工程 电信工程 电信工程
- 人工智能在通信中的应用
背景情况:
- 现有的多载波混沌加密方法面临的挑战是峰值到平均功率比 (PAPR) 和动态退化.
- 无线光纤无集成系统需要强大的物理层安全解决方案.
- 需要先进的加密技术来防止非法访问和数据拦截.
研究的目的:
- 提出一种基于光纤无线系统的变压器长短时间 (LSTM) 神经网络的新型音调保留 (TR) 加密方案.
- 解决 PAPR 和混沌加密中的动态退化问题.
- 为了实现高数据速率的安全物理层加密传输.
主要方法:
- 集成23个大规模的3D混乱模型与混合变压器-LSTM神经网络架构.
- 通过用于加密的非线性预测建模生成3D混乱序列.
- 混沌序列的应用用于TR选择,有效的子载体编码和整个子载体编码.
主要成果:
- 实现了物理层加密,平均PAPR降低1.47dB.
- 神经网络预测准确度:R平方>0.99,RMSE=0.066. 神经网络预测准确度:R平方>0.99,RMSE=0.066.
- 证明了12Gbaud QPSK信号的安全传输速度为20.6 Gb/s,BER达到HD-FEC值,最低BER值为2.1×10−5.
- 输入光功率的灵敏度提高了0.45dB.
- 非法接收者BER超过0.4,证实了对未经授权访问的有效安全性.
结论:
- 拟议的基于变压器LSTM的TR加密方案有效地确保了物理层的数据传输.
- 该方案成功地减轻了PAPR和动态降解问题,同时保持了高通信性能.
- 这种方法显示了未来光纤无线集成系统中安全通信的巨大潜力.
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