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.
By Oier Larumbe-Lizarraga, Roberto Pereira, Cristian J. Vaca-Rubio
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...
By Mohamad Alkadamani, Halim Yanikomeroglu
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.
By Niraj Gadhe, Kirti Bhardwaj, Moulik Jain, Shubhi Sharma, Vinay Saini
arXiv:2606. 28274v1 Announce Type: cross Abstract: Accurate network traffic prediction is a critical element for efficient resource allocation in dynamic urban cellular networks.
By Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast
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.
By Zhanyu Ju, Wenchi Cheng
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.
By Natalia Vesselinova, Pauliina Ilmonen
arXiv:2609.00284v1 Announce Type: cross
Abstract: Emerging 6G wireless networks are expected to operate across diverse deployment scenarios, where variations in network topology, user mobility, traff...
By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
By Sarah Al-Shareeda, Gulcihan Ozdemir, Heung Seok Jeon
Halo is a modification to existing deep forecasters that adds a second output for estimating the scale of the predicted distribution, trained with a matching negative log likelihood. Experiments on five electricity price markets show that Halo improves mean squared error and mean absolute error in 28 of 30 model‑market‑metric comparisons, with average MSE reductions of 2.6% to 16.5% and MAE reductions of 1.7% to 11.0%. The study finds that the source of the scale estimate is less important than the fact that the network estimates scale, and that the improvement persists without retuning hyperparameters.
By Adam Cataldo
arXiv:2607. 14871v1 Announce Type: cross Abstract: In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction.
By Theivaprakasham Hari, Yanan Xin, Winnie Daamen, Serge Paul Hoogendoorn, Sascha Hoogendoorn-Lanser
arXiv:2606. 19821v1 Announce Type: new Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks.
By Geon Kim, Dara Ron, Sukhdeep Singh, Suyog Moogi, Pranshav Gajjar, V V N K Someswara Rao Koduri, Een Kee Hong, Vijay K. Shah
arXiv:2606. 13513v1 Announce Type: new Abstract: Driven by conservative over-provisioning to guarantee service reliability, resource utilization in cloud data centers remains at low levels.
By Xiaobin Zhang, Lefei Shen, Mouxiang Chen, Zhuo Li, Hongkai Li, Han Fu, Jianling Sun, Xiaoxue Ren, Chenghao Liu