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双输入双拼接网络与数据增强,用于在通信对策中强大的调制识别
1AirForce Communication NCO Academy, DaLian, 116600, China. 799599848@qq.com.
Scientific reports
|July 29, 2025
概括
这项研究引入了一种新的双拼接深度学习模型,用于准确的调制识别. 该方法在各种信号噪声比率 (SNR) 中提高了性能,并优于现有技术.
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:
- 准确的调制识别对于通信侦察至关重要.
- 当前的方法在不同的信号噪声比 (SNR) 条件下面临挑战.
- 现有的特征提取技术可以提高准确性,但增加处理时间.
研究的目的:
- 在低和高SNR场景中开发具有高精度的调制识别方法.
- 通过优化数据集特征和网络模型设计来提高调制识别性能.
- 创建一种超越现有方法的新型深度学习模型.
主要方法:
- 集成的数据增强和振幅相特性用于数据集增强.
- 复制并比较基线调制识别模型以确定关键特征.
- 优化了基于已识别的高性能模型和不同的SNR,数据集和网络层的深度学习模型设计,从而产生了双拼接架构.
- 在 -20-0 dB 和 0-20 dB SNR 间隔中对六个数据集进行模型测试.
主要成果:
- 拟议的双拼接深度学习模型在六个数据集上取得了显著的结果.
- 在识别率方面表现优于其他11种调制识别方法.
- 部分解决了AM-DSB/AM-SSB和16QAM/64QAM等类似调制方案之间的识别混.
结论:
- 新的双拼接深度学习模型在广泛的SNR中提供了卓越的调制识别精度.
- 该方法有效地解决了现有方法的局限性,特别是在具有挑战性的低SNR环境中.
- 这项工作在通信侦察技术方面取得了重大进展.
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