RF Fingerprinting of DSP Implementations using Machine Learning: An Over-the-Air Case Study
Radio frequency fingerprinting aims to identify transmitters by exploiting small differences in the emitted signal waveform. Common approaches exploit hardware-dependent imperfections for that purpose. This work instead investigates whether differences between digital signal processing implementations of a transmitter can be identified from their radiated signals. As a practical case study, five different software-based DSP implementations for generating a 2-FSK RTTY communication waveform are considered. In a monitoring receiver, a classifier can identify these DSP implementations using convolutional neural networks that are trained on augmented software-generated signals represented as ...
| Authors | Stefan Scholl |
|---|---|
| Primary category | eess.SP |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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