Machine Learning in NextG Networks via Generative Adversarial Networks

TL;DR

This paper surveys Generative Adversarial Networks (GANs) for NextG wireless networks, focusing on spectrum sharing, anomaly detection, and security. It covers GAN fundamentals, variants (CGAN, LSGAN, WGAN, WGAN-GP, BiGAN), and anomaly-detection frameworks (AnoGAN, EGBAD, GANomaly, f-AnoGAN), emphasizing the minimax training objective $ \min_G \max_D V(D,G) $ and latent-space mappings. It reviews data-augmentation use-cases, domain adaptation, and multiple RF tasks, and presents simulation results showing GAN-based anomaly detection can outperform conventional methods in modulation classification. The paper also outlines datasets, performance measures, and future research directions, highlighting open challenges in training stability, fidelity metrics, and scalability for NextG systems.

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, 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.

Figures (10)

• Figure 1: Generative Adversarial Network (GAN) with image generation application Silva18.
• Figure 2: Generative Adversarial Network (GAN) with image generation application Silva18.
• Figure 3: The structure of BiGAN DKD16.
• Figure 4: Simplified single-dimensional architecture representation of GANomaly. Multiple blocks in series represent two-dimensional convolutional encoders and single blocks are for providing two-dimensional input-output AAB18.
• Figure 5: Comparison of GANs for anomaly detection for computer vision and image processing applications, A: AnoGAN SSWSL17, B: EGBAD ZFLMC18, C: GANomaly AAB18. Two-dimensional convolutional encoders and input-output devices are depicted as single-dimensional blocks for simplicity without any loss in functionality.