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Machine Learning in NextG Networks via Generative Adversarial Networks. (arXiv:2203.04453v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
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 …
arxiv generative adversarial networks learning machine machine learning networks