一个轻量级的双分支复杂估值神经网络用于通信信号的自动调制分类
Zhaojing Xu1, Youchen Fan1, Shengliang Fang1
1School of Space Information, Space Engineering University, Beijing 101416, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
一个新的轻量级双分支复杂值神经网络 (LDCVNN) 显著推进了自动调制分类 (AMC). 这种高效的模型以最小的参数实现了高精度,克服了信号处理中的部署挑战.
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:
- 深度学习,特别是复杂值神经网络 (CVNNs),由于其特征提取能力,对自动调制分类 (AMC) 是关键的.
- 在处理复杂的通信信号方面,CVNN提供了优势,捕获了振幅和相位信息.
- 现有的AMC的CVNN模型存在高参数数量和计算复杂性,这阻碍了实际部署.
研究的目的:
- 引入一种新的轻量级双分支复杂值神经网络 (LDCVNN),以实现高效准确的AMC.
- 解决现有模型在参数数量和计算负载方面的局限性.
- 在复杂的通信信号处理中增强特征提取和分类性能.
主要方法:
- 提出了一种双分支架构,以分别处理相位信息和复杂缩放等差表示.
- 利用可训练加权聚变,以适应性地结合两个分支的特征.
- 将扩展空间和通道重建卷积 (SCConv) 扩展到复杂域,结合复杂值深度可分离卷积块 (CBlock) 和平均聚合以优化特征.
主要成果:
- 在RML2016.10a数据集上,LDCVNN仅在9.0K参数和没有数据增强的情况下实现了最高的平均精度.
- 证明了显著的参数减少:与CDSN相比99.33%,与CSDNN相比97.25%.
- 在多个数据集中展示了效率和性能之间的卓越平衡.
结论:
- LDCVNN为自动调制分类提供了高效和有效的解决方案.
- 该模型显著降低了计算复杂性和参数数量,使其适合在资源有限的环境中部署.
- 拟议的架构推进了无线通信信号处理的最新技术.
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