Resource-constrained narrowband optoelectronic oscillator-based reservoir computing for classification of modulated signals.

We experimentally investigate the performance of narrowband optoelectronic oscillator (OEO) reservoir computers using the standard 10th-order nonlinear autoregressive-moving-average (NARMA10) task. Because comparing results from differently parameterized photonic time-delay systems can be difficult, we introduce a new, to the best of our knowledge, metric that accounts for system size, computational accuracy, and training effort overhead in order to provide an "at-a-glance" method to holistically determine a reservoir computer's performance. We then demonstrate the first experimental effort of narrowband OEO-based reservoir computing for the RADIOML dataset, which consists of recognizing and classifying IQ-modulated radio signals including analog and digital modulations. Our results indicate that narrowband OEOs are capable of achieving reasonable accuracies with exceptionally small training sets, thereby paving the way to real-time machine learning for radio frequency signals.