基于高效设计和网络压缩的资源有限的特定发射器识别
Mengtao Wang1, Shengliang Fang2, Youchen Fan2
1Graduate School, Space Engineering University, Beijing 101416, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了使用高效设计和模型压缩的资源受限特定发射者识别 (RC-SEI) 方法. 轻量级卷积网络 (LCNet) 在物联网应用中实现了高精度,显著降低了模型复杂性.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 计算机工程 计算机工程
背景情况:
- 深度学习 (DL) 增强了复杂信号的特定发射者识别 (SEI).
- 高模型复杂性和特征维度限制了基于DL的SEI在资源受限 (RC) 边缘设备上,特别是在物联网 (IoT) 中.
研究的目的:
- 开发一种高效和压缩的SEI方法,适用于RC边缘设备.
- 解决基于DL的SEI中的模型参数冗余性和高特征维度性的局限性.
主要方法:
- 提出了一种轻量级卷积网络 (LCNet),以实现高效的设计,平衡性能和复杂性.
- 在完全连接的层中实现了稀疏规则化,以减少超过99%的特征维度.
- 评估了公共自动依赖监控广播 (ADS-B) 和Wi-Fi数据集的方法.
主要成果:
- LCNet实现了高识别准确度:在ADS-B上达到99.40%,在Wi-Fi上达到99.90%.
- 该模型的复杂性显著降低,只有33,510 (ADS-B) 和33,544 (Wi-Fi) 参数.
- 与现有方法相比,拟议的方法在准确性和模型复杂性方面表现出优异的性能.
结论:
- 开发的RC-SEI方法对于资源有限的场景是可行的和有效的.
- 在物联网应用中部署先进的SEI,LCNet提供了一个有前途的解决方案.
- 有效的设计和模型压缩对于实际的边缘AI实现至关重要.
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