Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation

Article


Navidan, H., Fard Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. 2021. Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation. Computer Networks. 194 (Art. 108149). https://doi.org/10.1016/j.comnet.2021.108149
AuthorsNavidan, H., Fard Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D.
Abstract

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.

KeywordsGenerative Adversarial Networks; Deep Learning; Semi-supervised Learning; Computer Networks; Communication Networks
JournalComputer Networks
Journal citation194 (Art. 108149)
ISSN1389-1286
Year2021
PublisherElsevier
Accepted author manuscript
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1016/j.comnet.2021.108149
Publication dates
Online04 May 2021
Print20 Jul 2021
Publication process dates
Accepted29 Apr 2021
Deposited07 Jun 2021
Copyright holder© 2021 Elsevier
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