mlsanityData to 14 Oct 2026

Machine Learning in NextG Networks via Generative Adversarial Networks

arXiv 2203.04453 ↗First posted 9 Mar 2022cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 87 of its 183 references that are in the map. First posted on arXiv 9 Mar 2022.

Builds on

87 of 183 references.

References · 87

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38 more
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50 most cited of 87 All 87 (JSON)

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Cited by year

Citing papers per year: 2022: 3; 2023: 1; 2024: 3; 2025: 1.

8 papers.

  1. 2023From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscapecited by 18
  2. 2022Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learningcited by 11
  3. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  4. 2024Deep Learning Frameworks for Cognitive Radio Networks: Review and Open Research Challengescited by 2
  5. 2022Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharingcited by 1
  6. 2024Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signalscited by 1
  7. 2022Adversarial Machine Learning-Based Anticipation of Threats Against Vehicle-to-Microgrid Servicescited by 0
  8. 2025Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentationcited by 0