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基于深度传输学习的变量多普勒水下声通信.

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  • 1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.

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概括
此摘要是机器生成的。

这项研究介绍了一种使用深度转移学习和卷积神经网络进行直接信号调节的新型水下声通信系统. 该系统表现出卓越的性能,特别是在可变运动场景中.

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科学领域:

  • 水下声学通讯水下声学通信
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 由于多普勒效应和多路径传播,水下声通信面临着挑战.
  • 传统系统通常需要复杂的多普勒估计,限制了动态环境中的性能.

研究的目的:

  • 提出一个基于深度转移学习 (DTL) 的系统,用于可变多普勒频率跳跃的二元频率转移键的水下声学通信.
  • 开发一个采用卷积神经网络 (CNN) 的接收器,用于直接信号解调,绕过显式多普勒估计.

主要方法:

  • 通过对模拟数据预训练CNN并对特定通信场景数据进行微调,利用DTL.
  • 我为CNN使用Mel谱图来学习频率跳跃组中的频率到符号映射.
  • 实现了CNN作为接收器的调节模块.

主要成果:

  • 与传统方法相比,拟议的DTL-CNN系统取得了更高的性能.
  • 在浅水声道的变速运动条件下,性能增长尤其显著.
  • 没有多普勒估计的直接解调证明是有效的.

结论:

  • 基于DTL的CNN方法为水下声通信系统提供了强大而高效的解决方案.
  • 这种方法在具有可变多普勒位移的动态水下环境中提高了通信可靠性.