Publications

Impact of Later-Stages COVID-19 Response Measures on Spatiotemporal Mobile Service Usage

The COVID-19 pandemic has affected our lives and how we use network infrastructures in an unprecedented way. While early studies have started shedding light on the link between COVID-19 containment measures and mobile network traffic, we presently lack a clear understanding of the implications of the virus outbreak, and of our reaction to it, on the usage of mobile apps. We contribute to closing this gap, by investigating how the spatiotemporal usage of mobile services has evolved through different response measures enacted in France during a continued seven-month period in 2020 and 2021. Our work complements previous studies in several ways: (i) it delves into individual service dynamics, whereas previous studies have not gone beyond broad service categories; (ii) it encompasses different types of containment strategies, allowing to observe their diverse effects on mobile traffic; (iii) it covers both spatial and temporal behaviors, providing a comprehensive view on the phenomenon. These elements of novelty let us lay new insights on how the demands for hundreds of different mobile services are reacting to the new environment set forth by the pandemics.

DeepRay: Deep Learning Meets Ray-Tracing

Efficient and accurate indoor radio propagation modeling tools are essential for the design and operation of wireless communication systems. Lately, several attempts to combine radio propagation solvers with machine learning (ML) have been made. In this paper, motivated by the recent advances in the area of computer vision, we present a new ML propagation model using convolutional encoder-decoders. Specifically, we couple a ray-tracing simulator with either a U-Net or an SDU-Net, showing that the use of atrous convolutions utilized in SDU-Net can significantly enhance the performance of an ML propagation model. The proposed data-driven framework, called DeepRay, can be trained to predict the received signal strength in a given indoor environment. More importantly, once trained over multiple input geometries, DeepRay can be employed to directly predict the signal level for unknown indoor environments. We demonstrate this approach in various indoor environments using long range (LoRa) devices operating at 868 MHz.

EM DeepRay: An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next-generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper, we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decoding it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data.

Pseudo Ray-Tracing: Deep Leaning Assisted Outdoor mm-Wave Path Loss Prediction

In this letter we present our results on how deep learning can be leveraged for outdoor path loss prediction in the 30GHz band. In particular, we exploit deep learning to boost the performance of outdoor path loss prediction in an end-to-end manner. In contrast to existing 3D ray tracing approaches that use geometrical information to model physical radio propagation phenomena, the proposed deep learning-based approach predicts outdoor path loss in the urban 5G scenario directly. To achieve this, a deep learning model is first trained offline using the data generated from simulations utilizing a 3D ray tracing approach. Our simulation results have revealed that the deep learning based approach can deliver outdoor path loss prediction in the 5G scenario with a performance comparable to a state-of-the-art 3D ray tracing simulator. Furthermore, the deep learning-based approach is 30 times faster than the ray tracing approach.