RF Waveform Synthesis Guided by Deep Reinforcement Learning

In this work, we demonstrate a system that enhances radio frequency (RF) fingerprints of individual transmitters via waveform modification to uniquely identify them amidst an ensemble of identical transmitters. This has the potential to enable secure identification, even in the presence of stolen and retransmitted unique device identifiers that are present in the transmitted waveforms, and ensures robust communications. This approach also lends itself to steganography as the waveform modifications can themselves encode information. Our system uses Bayesian program learning to learn specific characteristics of a set of emitters, and integrates the learned programs into a reinforcement learning architecture to build a policy for actions applied to the digital waveform before transmission. This allows the system to learn how to modify waveforms that leverage and emphasize inherent differences within RF front-ends to enhance their distinct characteristics while maintaining robust communications. In this ongoing research, we demonstrate our system in a small population, and provide a road map to expand it to larger populations that are expected in today's interconnected spaces.