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

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

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.

• Jezik: Angleški jezik
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2021
• Št. strani: Str. 1-30
• Številčenje: Vol. 16, iss. 11
• PID: 20.500.12556/RUL-134714
• UDK: 659.2:004
• ISSN pri članku: 1932-6203
• DOI: 10.1371/journal.pone.0260308
• COBISS.SI-ID: 86681091
• Datum objave v RUL: 27.01.2022

CASTELLI, Mauro, MANZONI, Luca, ESPINDOLA, Tatiana, POPOVIČ, Aleš in DE LORENZO, Andrea, 2021, Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. PloS One [na spletu]. 2021. Vol. 16, no. 11, p. 1–30. [Dostopano 5 maj 2025]. DOI 10.1371/journal.pone.0260308. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=134714

Gradivo je del revije

• Naslov: PloS One
• Založnik: Public Library of Science
• ISSN: 1932-6203
• COBISS.SI-ID: 2005896

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 23.11.2021

Projekti

• Financer: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Številka projekta: DSAIPA/DS/0022/2018
• Naslov: GADgET

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Program financ.: Raziskovalni program
• Številka projekta: P5-0410
• Naslov: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe