一个复杂值的卷积融合类型的多流时空网络,用于自动调制分类
Yuying Wang1, Shengliang Fang2, Youchen Fan3
1Graduate School, Space Engineering University, Beijing, 101416, China.
Scientific reports
|September 27, 2024
概括
本研究介绍了一种用于无线通信中的自动调制分类 (AMC) 的新型网络. 拟议的方法显著提高调制识别精度,特别是在具有挑战性的低信号噪声比环境中.
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) 对于识别非合作通信系统中的信号至关重要.
- 深度学习 (DL) 已经推进了AMC,但在利用In-phase (I) 和Quadrature-phase (Q) 组件关系以在低信号对噪声比率 (SNR) 上获得准确性方面仍然存在挑战.
研究的目的:
- 为AMC开发一个先进的网络,以提高识别准确性,特别是在低SNR条件下.
- 利用通信信号的空间和时间特征来改进调制识别.
主要方法:
- 引入一个复杂值的卷积融合型多流空间时空网络 (CC-MSNet).
- 在CC-MSNet架构中集成空间和时间特征提取模块.
- 在基准数据集上的评估:RML2016.10a,RML2016.10b和RML2016.04c.
主要成果:
- CC-MSNet实现了62.86% (RML2016.10a),65.08% (RML2016.10b) 和71.12% (RML2016.04c) 的平均识别准确率.
- 该网络在低SNR环境 (0dB以下) 中表现出色.
- 在具有挑战性的低SNR条件下,CC-MSNet显著优于现有网络.
结论:
- 拟议的CC-MSNet有效地提高了非合作通信系统的调制识别精度.
- 网络结合空间和时间特征的能力是其卓越性能的关键,特别是在低SNR时.
- CC-MSNet代表了AMC的重大进步,在不利的信号条件下提供了强大的性能.
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