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基于深度学习的自动调制分类使用强大的CNN架构用于认知无线电网络.

Ola Fekry Abd-Elaziz1, Mahmoud Abdalla1,2, Rania A Elsayed1

  • 1Electronics and Communications Engineering, Zagazig University, Zagazig 44519, Egypt.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一个强大的卷积神经网络 (CNN),用于智能接收器中的自动调制分类 (AMC). 新的CNN架构增强了特征提取,即使在具有挑战性的低SNR无线环境中也实现了高精度.

关键词:
和无线通道损害.自动调制分类自动调制分类.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.原始智商序列的原始智商序列

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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:

  • 自动调制分类 (AMC) 对于非合作通信系统中的智能接收器至关重要,包括认知无线电和军事应用.
  • 现有的AMC方法在各种现实无线通道损伤下准确分类调制方案方面面临挑战.

研究的目的:

  • 提出一个强大的自动调制分类模型,使用一种新的卷积神经网络 (CNN) 架构.
  • 增强特征提取能力,以在复杂的无线环境中改进调制识别.

主要方法:

  • 开发了一种新的CNN架构,其中具有不对称的卷积内核的并行组合.
  • 在CNN架构中实现了跳过连接,以减轻消失梯度的问题.
  • 在各种通道损伤下 (AWGN,Rician色,时钟偏移) 对九个调制方案评估了模型的性能.

主要成果:

  • 拟议的CNN模型在不同SNR中实现了高分类精度,在-2dB时达到86.1%,在0dB时达到96.5%,在10dB时达到99.8%.
  • 与模块化类型识别中现有的最先进方法相比,证明了卓越的性能.
  • 展示了强大的特征提取能力,在挑战性16QAM和64QAM信号方面达到81.02%的平均精度.

结论:

  • 新的CNN架构为自动调制分类提供了强大而有效的解决方案.
  • 该模型在低SNR的增强性能使其适合于现实的非合作通信场景.
  • 该架构在类似的调制方案之间进行区分的能力突出了其先进的特征提取功率.