Signal quality detection in mobile network based on synthetic vehicle location data generation using generative adversial neural networks

• Published in: 2024 International Wireless Communications and Mobile Computing (IWCMC) pp. 0025 - 0030
• Main Authors: Kassan, Sara, Mnakri, Bechir, Nagellen, Thierry, Guyard, Frederic, Tosic, Tamara
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.05.2024
• Subjects: Adaptation models; Data models; Data privacy; GANs models; Generative adversarial networks; mode specific normalization; OverPy tool; privacy preservation; Quality of service; Signal map; SNR; Solid modeling; traffic mobility data; Training
• ISSN: 2376-6506
• DOI: 10.1109/IWCMC61514.2024.10592448

Connected and autonomous vehicles can be highly affected by sudden changes in the provided mobile network coverage. Driving safety increases with a high Signal-to-Noise Ratio (SNR) that enhances the Quality of Service (QoS). Therefore, monitoring the QoS for geolocated vehicular mobility from mobile phones is required to detect position and timestamp of QoS changes, and alert the operators to improve the QoS. However, mobility data sharing raises privacy concerns, which, in turn, limits accessibility to the data. This article proposes an adapted Generative Adversarial Network (GAN) model combined with OverPy tool to generate SNR data associated with real vehicle locations, to enrich the dataset while guaranteeing the privacy of users. The dataset used in this paper to train the model is real opt-in data collected from the Signal Map mobile application from volunteers in the region of Nice (France). The training process of the model is iterative and involves updating the parameters of the generator and discriminator in a way that improves the overall performance of the model. The preliminary evaluation results show that the model generates high-quality traffic mobility data correlated to SNR parameter. Specifically, the synthetic generated data have statistical properties similar to real data reflecting real mobility statistics, and therefore protecting the individual's privacy.