  High-quality recordings of radio frequency (RF) emissions from commercial
communication hardware in realistic environments are often needed to develop
and assess spectrum-sharing technologies and practices, e.g., for training and
testing spectrum sensing algorithms and for interference testing.
Unfortunately, the time-consuming, expensive nature of such data collections
together with data-sharing restrictions pose significant challenges that limit
data set availability. Furthermore, developing accurate models of real-world RF
emissions from first principles is often very difficult because system
parameters and implementation details are at best only partially known, and
complex system dynamics are difficult to characterize. Hence, there is a need
for flexible, data-driven methods that can leverage existing data sets to
synthesize additional similar waveforms. One promising machine-learning
approach is unsupervised deep generative modeling with generative adversarial
networks (GANs). To date, GANs for RF communication signals have not been
studied thoroughly. In this paper, we present the first in-depth investigation
of generated signal fidelity for GANs trained with baseband orthogonal
frequency-division multiplexing (OFDM) signals, where each subcarrier is
digitally modulated with quadrature amplitude modulation (QAM). Building on
prior GAN methods, we developed two novel GAN models and evaluated their
performance using simulated data sets with known ground truth. Specifically, we
investigated model performance with respect to increasing data set complexity
over a range of OFDM parameters and conditions, including fading channels. The
findings presented here inform the feasibility of use cases and provide a
foundation for further investigations into deep generative models for RF
communication signals.

Signal Processing