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使用基于深度神经网络的接收器进行水下声学直角频率分割复杂通信:河流试验结果

Sabna Thenginthody Hassan1, Peng Chen1, Yue Rong1

  • 1School of Electrical Engineering, Computing and Mathematical Sciences (EECMS), Faculty of Science and Engineering, Curtin University, Bentley, WA 6102, Australia.

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概括

一个新的深度神经网络 (DNN) 接收器通过学习和补偿通道非线性来改善水下声学 (UA) 通信. 这种基于DNN的方法优于传统方法,为未来的UA系统提供了更高的可靠性和适应性.

关键词:
多普勒转移是多普勒转移的一种转移.卷积神经网络是一种卷积神经网络.深度神经网络是一个神经网络.最小平方的最小平方.长期短期记忆 长期短期记忆正角频率划分多重复合.水下声波通信是水下声波通信.

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 信号处理 信号处理

背景情况:

  • 水下声学 (UA) 通信面临挑战,原因是多路径传播导致显著延迟传播.
  • 传统的直角频率分割多重复合 (OFDM) 接收器由于UA通道中的试点子载波之间的非线性频率响应而难以进行通道估计.
  • 水下通道延迟形状的未知性质阻碍了对这种非线性进行精确的建模.

研究的目的:

  • 提出和评估基于深度神经网络 (DNN) 的接收器,用于水下声学 (UA) 通信.
  • 通过利用神经网络能力来解决UA通信中非线性通道响应的挑战.
  • 为了证明基于DNN的接收器在现实世界UA环境中比传统方法提高了性能.

主要方法:

  • 开发一个深度神经网络 (DNN) 架构用于水下声学 (UA) 通信接收器.
  • 训练神经网络 (NN) 来学习和补偿 UA 通信中固有的通道非线性.
  • 与使用河流试验数据的传统最小平方 (LS) 估计器接收器进行比较性性能分析.

主要成果:

  • 基于DNN的UA通信接收器与传统的最小平方 (LS) 估计器相比,表现优越.
  • 西澳大利亚的河流试验验证了拟议的基于DNN的接收器的有效性.
  • 结果表明,神经网络成功地学习并对非线性通道效应进行补偿.

结论:

  • 深度神经网络 (DNN) 接收器为彻底改变水下声学 (UA) 通信提供了一个有希望的解决方案.
  • 拟议的基于DNN的方法可以实现更高的数据速率,更高的可靠性和更好的适应力,以适应动态的水下条件.
  • 未来的UA通信系统可以从DNN接收器技术的整合中获得显著的好处.