Generative adversarial networks for generating synthetic features for Wi-Fi signal quality.
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 part...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260308&type=printable |
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| author | Mauro Castelli Luca Manzoni Tatiane Espindola Aleš Popovič Andrea De Lorenzo |
| author_facet | Mauro Castelli Luca Manzoni Tatiane Espindola Aleš Popovič Andrea De Lorenzo |
| author_sort | Mauro Castelli |
| collection | DOAJ |
| description | 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 compared 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. |
| format | Article |
| id | doaj-art-2a8ccd2c1f07470991cfd28226ca5725 |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-2a8ccd2c1f07470991cfd28226ca57252025-08-20T02:55:20ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011611e026030810.1371/journal.pone.0260308Generative adversarial networks for generating synthetic features for Wi-Fi signal quality.Mauro CastelliLuca ManzoniTatiane EspindolaAleš PopovičAndrea De LorenzoWireless 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 compared 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.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260308&type=printable |
| spellingShingle | Mauro Castelli Luca Manzoni Tatiane Espindola Aleš Popovič Andrea De Lorenzo Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. PLoS ONE |
| title | Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. |
| title_full | Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. |
| title_fullStr | Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. |
| title_full_unstemmed | Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. |
| title_short | Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. |
| title_sort | generative adversarial networks for generating synthetic features for wi fi signal quality |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260308&type=printable |
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