Zineddine Bettouche

Submitted: 20 January 2026 You are already at the latest version Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training and maintenance costs, while global models often fail to capture heterogeneous spatial dynamics.

[Submitted on 17 Jul 2025] Title:Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting View a PDF of the paper titled Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting, by Khalid Ali and 3 other authors View PDF HTML (experimental) Abstract:Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond.