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The paper introduces a goal‑oriented probabilistic forecasting framework for dynamic physical resource block (PRB) allocation in 5G networks. By training DeepAR and Temporal Fusion Transformer models with the Pinball Loss function and selecting an optimal allocation quantile based on an operator’s cost matrix, the approach aligns forecasting with operational objectives. Experiments on real beam‑level 5G traffic data show reduced operational cost compared to MSE‑trained baselines while preserving calibrated uncertainty estimates.
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. 19974v1 Announce Type: cross Abstract: The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning critical for maintaining optimal network performance.
arXiv:2606. 28274v1 Announce Type: cross Abstract: Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks.
arXiv:2608. 08804v1 Announce Type: cross Abstract: With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks.
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.