Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

Castelli, Mauro (author), Manzoni, Luca (author), Espindola, Tatiana (author), Popovič, Aleš (author), De Lorenzo, Andrea (author)

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and com%pared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

• Language: English
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: Str. 1-30
• Numbering: Vol. 16, iss. 11
• PID: 20.500.12556/RUL-134714
• UDC: 659.2:004
• ISSN on article: 1932-6203
• DOI: 10.1371/journal.pone.0260308
• COBISS.SI-ID: 86681091
• Publication date in RUL: 27.01.2022

Record is a part of a journal

• Title: PloS one
• Publisher: PLOS
• ISSN: 1932-6203
• COBISS.SI-ID: 2005896

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 23.11.2021

Projects

• Funder: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Project number: DSAIPA/DS/0022/2018
• Name: GADgET

• Funder: ARRS - Slovenian Research Agency
• Funding programme: Raziskovalni program
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe