arXiv:2606. 28274v1 Announce Type: cross Abstract: Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks.
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arXiv:2609.06636v1 Announce Type: cross Abstract: Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densi...
arXiv:2607. 07016v1 Announce Type: cross Abstract: Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems.
Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems. Real-world traffic often exhibits bursty endogenous dynamics and disturbances triggered by external urban events, which makes reliable prediction highly challenging.
arXiv:2606. 25709v1 Announce Type: new Abstract: Mobile cellular load forecasting is native to network resource optimization and delivery of services with reliability, latency and quality guarantees.
The paper introduces SCRR-Net, a scene-conditioned spatial relation routing framework designed to forecast urban cellular activity by jointly modeling heterogeneous spatiotemporal signals such as SMS usage, mobile network traffic, and call activity. SCRR-Net integrates a context encoder, a spatial graph expert routing module, a temporal Transformer encoder, and a task knowledge routing module, allowing urban contextual information to control spatial dependency selection and cross-task knowledge transfer. Experiments on Milano and Trento datasets show that SCRR-Net consistently outperforms competing methods across all three forecasting tasks while offering interpretable routing behaviors.
The paper introduces TransMod, a unified framework for forecasting urban mobility demand across multiple transportation modes. It creates a shared zone-level spatial representation to align systems with different spatial granularities, reducing structural mismatch and distributional shift. TransMod then learns transferable spatio-temporal patterns from data-rich source modes and adapts them to data-scarce target modes, improving forecasting performance when target data is limited.