使用feedforward反向传播的神经网络进行多普勒尺度估计在水下声学CP-OFDM通信中
Muhammad Muzzammil1,2,3, Shahzad Saleem4, Niaz Ahmed5
1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin, 15001, China.
Scientific reports
|December 12, 2025
概括
这项研究引入了一种新的神经网络,用于估计水下声通信中的多普勒效应. 前向反向传播的神经网络 (FBNN) 改善了对直角频率分割多重复合 (OFDM) 系统的多普勒尺度因子估计.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 水下通信是指水下通信.
背景情况:
- 直角频率分割多重复合 (OFDM) 是水下声学 (UWA) 通信的一个关键技术.
- UWA通道面临重大挑战,包括大型多路径和严重的多普勒效应,这降低了通信性能.
- 准确的多普勒尺度估计对于在UWA OFDM系统中减轻这些影响至关重要.
研究的目的:
- 为UWA循环前 (CP) OFDM系统中多普勒尺度估计提出一种新的前向反向传播神经网络 (FBNN) 实现.
- 用不同的反向传播的训练算法变体来评估拟议的FBNN的性能.
- 将FBNN方法与多普勒估计的传统方法进行比较.
主要方法:
- 设计了一个双层输入-输出前神经网络架构.
- 使用了三个训练算法变体:弗莱彻-里夫斯结合梯度 (CGF),波拉克-里比埃尔结合梯度 (CGP) 和结合梯度与威尔/比尔重启 (CGB).
- 根平均平方误差 (RMSE) 用于在各种多路径和信号噪声比 (SNR) 条件下评估性能.
主要成果:
- 拟议的FBNN通过将神经网络功能与合梯度训练算法的精度相结合,有效地估计多普勒比例因子.
- 在各种道条件下评估了性能,证明了FBNN方法的稳定性.
- 对比分析显示,基于FBNN的方法与基准传统技术相比,实现了竞争性或优异的性能.
结论:
- 在UWA OFDM通信中,FBNN实现为准确的多普勒尺度估计提供了一个有希望的解决方案.
- 神经计算和高级训练算法的结合增强了对多路径和多普勒效应的弹性.
- 这项工作有助于提高水下声通信系统的可靠性和效率.
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