MODELESS: MODulation rEcognition with LimitEd SuperviSion

Modulation recognition (modrec) is an essential transmitter fingerprinting task that enables future spectrum-sharing applications such as access management and enforcement. Traditional supervised modrec requires labeled training data for all target modulations, which cannot be readily met with the advent of new, customized and data-driven waveforms. Thus, a keystone question for the applicability of modrec is: Can we perform automatic recognition of previously unobserved modulations by adapting and reusing models that were trained on different but related modulations?To this end, we develop MODELESS (MODulation rEcognition with LimitEd SuperviSion) that exploits knowledge from observed modulations to classify samples from unobserved ones. Our solution is grounded in zero-shot transfer learning, which employs side information among observed and unobserved classes to transfer learned classifiers. In particular we quantify the similarity among the theoretical constellation diagrams of unobserved and observed modulations and employ them in a zero-shot transfer learning framework. Our framework is general, as it can produce predictions for arbitrary modulations as long as their theoretical constellations can be specified. We evaluate MODELESS on synthetic and real-world traces and in comparison with zero-shot counterparts from the literature. We demonstrate near-ideal classification accuracy in the majority of the testing cases and draw recommendations for future research into classification tasks with sub-par performance.