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. In general, the OEOs used for reservoir computing are wideband and are processing analog signals in the baseband. However, their hardware architecture is inherently inadequate to directly process radiotelecom or radar signals, which are modulated carriers. On the other hand, the high- $Q$ OEOs that have been developed for ultra-low phase noise microwave generation have the adequate hardware architecture to process such multi-GHz modulated signals, but they have never been investigated as possible reservoir computing platforms. In this article, we show that these high- $Q$ OEOs are indeed suitable for reservoir computing with modulated carriers. Our dataset (DeepSig RadioML) is composed with 11 analog and digital formats of IQ-modulated radio signals (BPSK, QAM64, WBFM, etc.), and the task of the high- $Q$ OEO reservoir computer is to recognize and classify them. 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 classification task with an accuracy better than the state-of-the-art, for a wide range of parameters. We also investigate in detail the effects of reducing the size of the training sets on the classification performance.

中文翻译：

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基于带窄带光电振荡器的储层计算的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）的储层计算，以数值方式对IQ调制的射频信号进行分类。通常，用于储层计算的OEO是宽带的，并且正在处理基带中的模拟信号。但是，它们的硬件体系结构本质上不足以直接处理作为调制载波的无线电信或雷达信号。另一方面，高 $ Q $ 已开发用于产生超低相位噪声微波的OEO具有足够的硬件体系结构来处理这种多GHz调制信号，但从未将它们作为可能的储层计算平台进行过研究。在本文中，我们证明了这些高 $ Q $ OEO确实适用于采用调制载波进行油藏计算。我们的数据集（DeepSig RadioML）由IQ调制的无线电信号（BPSK，QAM64，WBFM等）的11种模拟和数字格式组成， $ Q $ OEO油藏计算机将对其进行识别和分类。我们的数值模拟结果表明，与神经网络同类产品相比，采用更简单的架构，更小的训练集，更少的节点和更少的层， $ Q $ 对于各种参数，基于OEO的储层计算机执行此分类任务的精度要优于最新技术。我们还详细研究了减少训练集大小对分类性能的影响。