Machine Learning in NextG Networks via Generative Adversarial Networks

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.

arXiv comment47 pages, 7 figures, 12 tables

References in corpus (29)

• Adam: A Method for Stochastic Optimization
• Conditional Generative Adversarial Nets
• Explaining and Harnessing Adversarial Examples
• Intriguing properties of neural networks
• An Introduction to Variational Autoencoders
• NIPS 2016 Tutorial: Generative Adversarial Networks
• Training Generative Adversarial Networks with Limited Data
• Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
• Data Augmentation Generative Adversarial Networks
• Efficient GAN-Based Anomaly Detection
• Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
• MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation
• Deep Structured Energy Based Models for Anomaly Detection
• Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• Active Learning for Wireless IoT Intrusion Detection
• A Survey on GANs for Anomaly Detection
• Anomaly Detection with Adversarial Dual Autoencoders
• RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning
• Are Generative Classifiers More Robust to Adversarial Attacks?
• Application of Machine Learning for Channel based Message Authentication in Mission Critical Machine Type Communication
• Deep Neural Networks Meet CSI-Based Authentication
• Morse Code Datasets for Machine Learning
• Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks
• One-Class Adversarial Nets for Fraud Detection
• End-to-End Autoencoder Communications with Optimized Interference Suppression
• A Neural Network Detector for Spectrum Sensing under Uncertainties
• Statistical Inference Attack Against PHY-layer Key Extraction and Countermeasures

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