在转移学习中利用机器学习分类器,以进行少数拍摄调制识别
Song Li1, Yong Wang1, Jun Xiong1
1Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了一种混合转移学习 (HTL) 方法,用于几次射击调制识别 (FSMR). HTL有效地将深度学习与传统机器学习相结合,在数据稀缺的环境中表现优于其他方法.
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:
- 通信系统需要有效的调制识别.
- 由于有限的标记数据,深度学习方法在短暂的场景中面临挑战.
- 短拍调制识别 (FSMR) 对于实际应用至关重要.
研究的目的:
- 提出一种混合转移学习 (HTL) 方法,用于强大的少数射击调制识别 (FSMR).
- 在FSMR的HTL框架内评估传统机器学习分类器的性能.
- 解决传统深度学习在数据稀缺环境中的局限性.
主要方法:
- 一种混合转移学习 (HTL) 方法,结合了深度特征提取和传统机器学习 (ML) 分类器.
- 通过预培训从大规模数据集转移知识.
- 使用各种经典的ML分类器,包括K-最近的邻居.
主要成果:
- 建议的HTL方法在数据稀缺的环境中始终优于现有的基线方法.
- 在HTL范式中,K-近邻分类器展示了最强大和最可概括的性能.
- 参数分析为实际应用中的实际部署提供了洞察力.
结论:
- 在实际的,数据有限的场景中,HTL为可靠的少数拍摄调制识别 (FSMR) 提供了一个有希望的解决方案.
- 深度特征提取和稳定的ML分类器之间的协同作用提高了性能.
- 在HTL框架内,K-最近邻居被确定为FSMR的高效分类器.
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