基于光电子振荡器的资源受限窄带储存计算用于调制信号的分类
Optics letters
|July 1, 2024
概括
我们开发了一个新的指标来评估光电子振荡器 (OEO) 储存电脑. 窄带OEOs显示出对实时无线电频率信号机器学习的承诺,使用最小的训练数据.
科学领域:
- 光子学 是一个光子学.
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 储计算利用复杂的系统进行计算.
- 光电子振荡器 (OEO) 为时间延迟系统提供了独特的特性.
- 评估和比较光子储计算机存在挑战.
研究的目的:
- 在NARMA10任务上实验评估窄带OEO储计算机性能.
- 引入一个全方位的水库计算机性能评估的新型指标.
- 为了证明窄带OEO储计算用于射频信号分类的第一个实验应用.
主要方法:
- 实现一个窄带光电子振荡器作为一个储计算机.
- 在第十级非线性自回归移动平均线 (NARMA10) 基准任务上进行测试.
- 开发和应用一个新的性能指标,考虑系统大小,准确性和培训开销.
- 应用到RADIOML数据集用于IQ调制的无线电信号识别.
主要成果:
- 成功展示了窄带OEO储库计算的试验性演示.
- 对拟议的性能指标进行验证,用于比较储计算机.
- 在RADIOML数据集上实现了合理的分类准确性.
- 在非常小的训练集中表现出高性能.
结论:
- 窄带OEOs对于储库计算任务是有效的.
- 新的指标提供了一个关于水库计算机性能的整体视图.
- 基于OEO的储计算是实时无线电频率机器学习的可行方法.
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