基于轻量级网络MobileViT的无线通信信号的实时星座图像分类方法
Qinghe Zheng1, Sergio Saponara2, Xinyu Tian1
1School of Intelligent Engineering, Shandong Management University, Jinan, 250357 China.
Cognitive neurodynamics
|May 3, 2024
概括
本研究引入了一种新的深度学习方法,用于实时自动调制分类 (AMC),使用MobileViT神经网络和集群星座图像,在边缘设备上实现更高的效率.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 无线通信无线通信
背景情况:
- 认知无线电需要有效的自动调制分类 (AMC) 来感知和适应电磁环境.
- 实时AMC对于认知无线电系统的动态频谱访问和干扰管理至关重要.
研究的目的:
- 开发一种轻量级的深度学习模型,用于高效的实时自动调制分类 (AMC).
- 为了利用聚类星座图像在调制识别中进行强大的特征提取.
主要方法:
- 将I/Q序列转换为聚类星座图像,用于特征提取.
- 开发和部署一个轻量级的神经网络,MobileViT,用于实时图像分类.
- 在RadioML 2016.10a数据集上使用边缘计算平台进行验证.
主要成果:
- 移动ViT模型在边缘计算平台上展示了实时AMC的卓越性能和效率.
- 废弃性研究证实了该方法在学习率和批量大小的变化方面的稳定性.
- 这项研究首次部署了深度学习模型,用于在网络边缘实时调制方案的分类.
结论:
- 拟议的基于MobileViT的方法为实时自动调制分类提供了高效和强大的解决方案.
- 星座图像分析与轻量级深度学习相结合,是基于边缘的认知无线电应用的一个有希望的方向.
- 这项工作推进了边缘设备在无线通信智能信号处理方面的功能.
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