We numerically perform the classification of IQ-modulated radiofrequency signals using reservoir computing based on narrowband optoelectronic oscillators (OEOs) driven by a continuous-wave semiconductor laser.Our numerical simulations show that with a simpler architecture, a smaller training set, fewer nodes and fewer layers than their neural network counterparts, high-Q OEO-based reservoir computers perform this task with an accuracy better than the state-of-the-art, for a wide range of parameters.

中文翻译：

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基于窄带光电振荡器储层计算的 IQ 调制信号分类


我们使用基于连续波半导体激光器驱动的窄带光电振荡器 (OEO) 的储库计算对 IQ 调制射频信号进行数值分类。我们的数值模拟表明，通过更简单的架构、更小的训练集、更少的节点和更少的基于高 Q OEO 的油藏计算机比神经网络对应层具有更多的层数，对于各种参数，其精度优于最先进的技术。