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.