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.
arXiv:2607. 17747v1 Announce Type: cross Abstract: The increasing complexity of mobile networks necessitates intelligent and dynamic control strategies for efficient, energy-conserving management.
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.
arXiv:2608. 05346v1 Announce Type: cross Abstract: Time-sensitive networking (TSN) is increasingly integrated into mobile edge computing (MEC) to support applications with stringent latency requirements, such as extended reality (XR).
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:2608.28878v1 Announce Type: cross Abstract: This paper develops a hybrid offline-online multi-agent reinforcement learning framework based on decision transformers. The policy is first pretrain...
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.
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.
arXiv:2606. 16331v1 Announce Type: new Abstract: The integration of generative artificial intelligence with wireless communication and signal processing systems has opened new avenues for intelligent, data-driven decision-making in future 6G networks.
arXiv:2608. 05340v1 Announce Type: cross Abstract: Time-Sensitive Networking (TSN) and Mobile Edge Computing (MEC) hold strong potential for enabling ultra-reliable low-latency communication for time-sensitive applications, such as eXtended Reality (XR).
The paper introduces a hierarchical hybrid architecture combining large language models (LLMs) and multi-agent reinforcement learning (MARL) to manage heterogeneous unmanned aerial systems in low‑altitude wireless networks (LAWNs). An outer LLM‑driven loop interprets service requirements and operator intent to reconfigure objectives and resource priorities, while an inner MARL loop executes decentralized policies under the updated game. A logistics‑monitoring case study demonstrates the framework’s ability to coordinate diverse services and adapt to changing conditions without retraining the MARL policies.
The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.
arXiv:2606. 00417v1 Announce Type: cross Abstract: To meet the stringent requirements of emerging applications and the increasingly complex network management and operation, the Next Generation Mobile Networks (NextG), or 6G, will adopt an AI-native architecture on the Core Network (CN).