This project aims to develop deep generative models, an emerging topic in AI, for radio frequency (RF) waveforms collected from real-world communications hardware. This research will lead to cheaper, more realistic synthetic waveform generation for testing, modeling, and data sharing.
After initial investigations with simulated datasets, we plan to develop generative models using real datasets. Potential applications of this work include generation of waveforms for interference testing, characterization of closed-box communication systems, and signal obfuscation for data sharing.
I/Q data segments of duration 17.07 ms, 30.72 MS/s sampling rate, visualized as 1024 x 1024 spectrograms