Generative Adversarial Radio Spectrum Networks

Abstract

Simulating and imitating RF communications signals and systems is a core function of jammers, spoofers, and other attacks in wireless radio environments which seek to confuse spectrum users as to what is occurring in the spectrum around them. Replay attacks and "DRFMs" have long been commonly used to deceive and probe radio systems, however generative models introduce an interesting new angle wherein generative replay can now produce examples of signals of similar structure and properties to arbitrary signals which are not verbatim replays and which may be varied in an infinite number of ways. Further, as GANs have demonstrated a strong ability to learn distributions from complex scenes and datasets, we consider the task of full-band spectral generation in addition to single signal generation to validate and demonstrate the feasibility of such an approach, to refine the algorithmic approach, and to quantify and illustrate the capabilities of such an approach on modern day signal sets.