Generative Adversarial Networks applied to Telecom Data - Using GANs to generate synthetic features regarding Wi-Fi signal quality

Abstract

Wireless networks are, currently, one of the main technologies used to connect people. Considering the constant advancements in the field, the telecom operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common the establishment of partnerships with specialized technology companies that deliver software services to monitor the networks and identify faults and respective solutions. Although, a common barrier faced for these specialized companies is the lack of data to develop and test their products. This project’s purpose was to better understand Generative Adversarial Networks (GANs), an algorithm considered state-of-theart between the generative models, and test its usage to generate synthetic telecommunication data that can fill this gap. To do that, it was developed, trained and compared two of the most used GAN’s architectures, the Vanilla GAN and the WGAN. Both the models presented good results and was able to simulate datasets very similar to the real ones. The WGAN was chosen as the final model, but just for presenting a slightly and subjective better result on the descriptive analysis. In fact, the two models had very similar outputs and both can be used.