  Spectrum coexistence is essential for next generation (NextG) systems to
share the spectrum with incumbent (primary) users and meet the growing demand
for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service
(CBRS) band, where the 5G and beyond communication systems need to sense the
spectrum and then access the channel in an opportunistic manner when the
incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity
classifier based on a deep neural network is needed for low misdetection (to
protect incumbent users) and low false alarm (to achieve high throughput for
NextG). In a dynamic wireless environment, the classifier can only be used for
a limited period of time, i.e., coherence time. A portion of this period is
used for learning to collect sensing results and train a classifier, and the
rest is used for transmissions. In spectrum sharing systems, there is a
well-known tradeoff between the sensing time and the transmission time. While
increasing the sensing time can increase the spectrum sensing accuracy, there
is less time left for data transmissions. In this paper, we present a
generative adversarial network (GAN) approach to generate synthetic sensing
results to augment the training data for the deep learning classifier so that
the sensing time can be reduced (and thus the transmission time can be
increased) while keeping high accuracy of the classifier. We consider both
additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this
GAN-based approach can significantly improve both the protection of the
high-priority user and the throughput of the NextG user (more in Rayleigh
channels than AWGN channels).
