6 papers
Lightweight PID-Based Drift Mitigation for Cellular Traffic Forecasting
John Sengendo, Zineddine Bettouche, Khalid Ali +2
As mobile networks transition from Beyond 5G (B5G) towards 6G, accurate traffic forecasting is a prerequisite for improving network management. However, with increasing heterogenei…
Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer +1
Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need fo…
A Robust Scheduling of Cyclic Traffic for Integrated Wired and Wireless Time-Sensitive Networks
Ãzgür Ozan Kaynak, Andreas Kassler, Andreas Fischer +2
Time-Sensitive Networking (TSN) is a toolbox of technologies that enable deterministic communication over Ethernet. A key area has been TSN's time-aware traffic shaping (TAS), whic…
HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer +1
Cellular traffic forecasting is essential for network planning, resource allocation, or load-balancing traffic across cells. However, accurate forecasting is difficult due to intri…
Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting
Khalid Ali, Zineddine Bettouche, Andreas Kassler +1
Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond. However, conventional AI approaches often fail to capture the intricate s…
Direct Feature Access -- Scaling Network Traffic Feature Collection to Terabit Speed
Lukas Froschauer, Jonatan Langlet, Andreas Kassler
Real-time traffic monitoring is critical for network operators to ensure performance, security, and visibility, especially as encryption becomes the norm. AI and ML have emerged as…