Generative adversarial networks for generating synthetic features for Wi-Fi signal quality
| Autor: | Andrea De Lorenzo, Tatiane Espindola, Mauro Castelli, Aleš Popovič, Luca Manzoni |
|---|---|
| Přispěvatelé: | Castelli, Mauro, Manzoni, Luca, Espindola, Tatiane, Popovič, Aleš, DE LORENZO, Andrea, NOVA Information Management School (NOVA IMS), NOVA IMS Research and Development Center (MagIC), Information Management Research Center (MagIC) - NOVA Information Management School |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Service (systems architecture)
Computer science Distributed computing Field (computer science) Computer Architecture Software Animal Cells Computer Networks Wi-Fi Flowering Plants Neurons Multidisciplinary Artificial neural network Wireless network Eukaryota Plants Telecommunications Engineering and Technology Medicine Cellular Types Wireless Technology Network Analysis Algorithms Research Article Computer and Information Sciences Neural Networks Science Synthetic data Game Theory Artificial Intelligence Machine learning Humans General Vanilla business.industry Deep learning generative adversarial network Organisms Biology and Life Sciences deep learning Cell Biology Signaling Networks generative adversarial networks Cellular Neuroscience udc:659.2:004 Neural Networks Computer Artificial intelligence business Generative grammar Neuroscience |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP PloS one, vol. 16, no. 11, pp. 1-30, 2021. PLoS ONE, Vol 16, Iss 11, p e0260308 (2021) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Castelli, M., Manzoni, L., Espindola, T., Popovič, A., & De Lorenzo, A. (2021). Generative adversarial networks for generating synthetic features for Wi-Fi signal quality. PLoS ONE, 16(11), 1-30. [e0260308]. https://doi.org/10.1371/journal.pone.0260308 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. publishersversion published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|