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解决自动调制识别中的一些挑战:一个多层次的比较关系网络,结合类重建策略.

Zhao Ma1, Shengliang Fang2, Youchen Fan2

  • 1Graduate School, Space Engineering University, Beijing 101416, China.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于自动调制识别 (AMR) 的新元学习方法,以克服数据限制. 多层次比较关系网络与类重建 (MCRN-CR) 有效地处理认知沟通中的少数镜头场景.

关键词:
自动调制识别自动调制识别深度学习是一种深度学习.几次射击的学习学习关系网络 关系网络.

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科学领域:

  • 认知沟通是一种认知沟通.
  • 机器学习 机器学习
  • 信号处理 信号处理

背景情况:

  • 自动调制识别 (AMR) 对认知通信和无线安全至关重要.
  • 深度学习 (DL) 方法已经推进了AMR,但在有限的数据上扎 (少数射击学习).

研究的目的:

  • 为了解决自动调制识别中的少数镜头困境.
  • 提出一种新的对抗药物耐药性的meta-learning方法,但数据有限.

主要方法:

  • 开发了一个带有类重建的多级比较关系网络 (MCRN-CR).
  • 采用了查询和支持样本之间的等级特征提取和关系得分计算.
  • 在嵌入功能中集成了一个用于样本重建的自动编码器,使用编码器进行特征提取.
  • 利用了结合分类和重建损失的元学习范式.

主要成果:

  • 在AMR中,MCRN-CR方法显著缓解了小样本问题.
  • 在RadioML2018数据集上的实验结果表明,与现有方法相比,其性能优越.
  • 拟议的方法提高了基于DL的AMR在实际场景中的适用性.

结论:

  • MCRN-CR方法提供了一个强大的解决方案,用于几次拍摄的自动调制识别.
  • 这项工作通过在有限的数据上实现有效的AMR来推进认知沟通领域.
  • 拟议的技术显示了改善无线安全和通信系统的前景.