  Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms
that have the ability to address competitive resource allocation problems
together with detection and mitigation of anomalous behavior. In this paper, we
investigate their use in next-generation (NextG) communications within the
context of cognitive networks to address i) spectrum sharing, ii) detecting
anomalies, and iii) mitigating security attacks. GANs have the following
advantages. First, they can learn and synthesize field data, which can be
costly, time consuming, and nonrepeatable. Second, they enable pre-training
classifiers by using semi-supervised data. Third, they facilitate increased
resolution. Fourth, they enable the recovery of corrupted bits in the spectrum.
The paper provides the basics of GANs, a comparative discussion on different
kinds of GANs, performance measures for GANs in computer vision and image
processing as well as wireless applications, a number of datasets for wireless
applications, performance measures for general classifiers, a survey of the
literature on GANs for i)-iii) above, and future research directions. As a use
case of GAN for NextG communications, we show that a GAN can be effectively
applied for anomaly detection in signal classification (e.g., user
authentication) outperforming another state-of-the-art ML technique such as an
autoencoder.
