In this seminar, Serly will present her recent work, accepted in ICMLCN 2025.

The exponential growth of mobile data traffic demands efficient and scalable forecasting methods to optimize network performance. Traditional approaches, like training individual models for each Base Station (BS) are computationally prohibitive for large-scale production deployments. In this paper, we propose a scalable Deep Neural Networks (DNN) training framework for mobile network traffic forecasting that reduces input redundancy and computational overhead. We minimize the number of input probes (traffic monitors at Base Stations (BSs)) by grouping BS s with temporal similarity using K-means clustering with Dynamic Time Warping (DTW) as the distance metric. Within each cluster, we train a DNN model, selecting a subset of BSs as inputs to predict future traffic demand for all BSs in that cluster. To further optimize input selection, we leverage the well-known EXplainable Artificial Intelligence (XAI) technique, LayeR-wise backPropagation (LRP) to identify the most influential BS s within each cluster. This makes it possible to reduce the number of required probes while maintaining high prediction accuracy. To validate our newly proposed framework, we conduct experiments on two real-world mobile traffic datasets. Specifically, our approach achieves competitive accuracy while reducing the total number of input probes by approximately 81% compared to state-of-the-art predictors.

About Serly Moghadas

Serly Moghadas Gholian is a fourth-year Ph.D. student in Telematics Engineering at IMDEA Networks Institute and Universidad Carlos III de Madrid. She holds a Bachelor’s degree in Electrical Engineering from Urmia University of Technology and a Master’s degree in Telecommunications Engineering from Urmia University. Her research focuses on mobile networks, spatio-temporal traffic forecasting, and the application of Explainable AI (XAI) to improve the transparency and robustness of deep learning models in network management. Her work has been published in venues including IEEE INFOCOM and IEEE Transactions on Mobile Computing (TMC).

Este evento se impartirá en inglés