arXiv Machine Learning By Zacharias Veiksaar, Maxime Bouton

Temporally Consistent Graph Q-Networks for Intelligent Network Control

Read the original on arXiv Machine Learning →

arXiv:2606. 13848v1 Announce Type: cross Abstract: Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI
Jun 4

Generalizable Multi-Task Learning for Wireless Networks Using Prompt Decision Transformers

arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.

By Fatih Temiz, Shavbo Salehi, Melike Erol-Kantarci
arXiv Machine Learning
Aug 11

ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems

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 AI
Sep 18

AI-Driven Real-Time Relay Optimisation in Smart Urban NR-V2X Networks via Learning-to-Optimise Graph Neural Networks

The paper introduces an AI-driven Learning-to-Optimise framework that uses Graph Neural Networks to select real‑time multi‑hop relays in NR‑V2X networks for smart cities. By modelling the vehicular network as a graph and training a GINE model with MILP‑derived optimal decisions, the method achieves near‑optimal connectivity, improving connectivity by up to 11.3% and speeding up computation by up to 100×. This enables scalable, real‑time network control suitable for Industry 4.0 and smart‑city deployments.

By Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti, Simone Angelini