2022
DOI: 10.48550/arxiv.2203.04453
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Machine Learning in NextG Networks via Generative Adversarial Networks

Ender Ayanoglu,
Kemal Davaslioglu,
Yalin E. Sagduyu

Abstract: 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, … Show more

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