在相似和不同的无线电之间转移学习的行为
Braeden P Muller1, Brennan E Olds1, Lauren J Wong2
1Virginia Tech National Security Institute, Blacksburg, VA 24060, USA.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
转移学习 (TL) 对无线电频率机器学习 (RFML) 是有前途的. 这项研究评估了无线电之间传输学习的行为,发现自动调制分类 (AMC) 和特定发射器识别 (SEI) 的好处.
科学领域:
- 无线电频率工程 无线电频率工程
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 转移学习 (TL) 在各种机器学习 (ML) 领域,如NLP和计算机视觉中是有效的.
- 将TL扩展到射频 (RF) 领域,可以增强RFML算法用于频谱认识和传输识别.
- 与其他领域不同,TL的射频应用面临硬件扭曲和频道变化的挑战.
研究的目的:
- 评估TL在RFML中的可行性和性能.
- 评估在同质和异质无线电之间学习行为的可转移性.
- 调查TL的自动调制分类 (AMC) 和特定发射者识别 (SEI) 算法.
主要方法:
- 利用应用到RFML算法上的转移学习技术.
- 测试了类似 (同质) 和不同 (异质) 结构的无线电之间的模型可传输性.
- 使用合成数据和无线实验数据进行评估.
主要成果:
- 通过将TL应用于RFML算法,证明了有前途的性能好处.
- 展示了TL在AMC和SEI任务中的有效性.
- 在不同无线电类型中成功转移学习的行为.
结论:
- 转移学习是RFML的可行和有益的技术.
- TL可以提高模型的可移植性和性能,用于频谱情境意识和识别任务.
- 该研究证实了TL在克服RF特定挑战方面的价值.
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