Simplified Denoising for Robust Specific Emitter Identification of Preamble-based Waveforms

Internet of Things (IoT) deployments continue to grow at an accelerated rate, thus presenting a growing surface over which nefarious actors can conduct attacks. This disturbing revelation is exacerbated by the fact that roughly 70% of all IoT devices employ weak or no encryption. Deep learning (DL)-based Specific Emitter Identification (SEI) has been put forward as a possible approach by which to secure IoT devices and related infrastructures. This work presents a DL-based SEI approach that remains robust under degrading signal-to-noise ratio (SNR) conditions while greatly reducing the complexity that is typically associated with DL-based approaches. The presented approach achieves an average percent classification performance of 97% or higher for SNR values greater than or equal to 6 dB.