The paper introduces GREAT, a Graph Edge Attention Network that operates directly on edge space to handle asymmetric energy costs in the Electric Vehicle Routing Problem (EVRP). Integrated into a reinforcement learning framework, GREAT jointly optimizes routing and charging decisions, outperforming a strong HGS-based heuristic by up to 8.78% in energy savings and reducing runtime by a factor of 60. It also shows competitive performance against a custom adaptive large neighborhood search and achieves state‑of‑the‑art results on other asymmetric routing benchmarks such as TSP, CVRP, and Orienteering.
By Attila Lischka, Filip Rydin, Jiaming Wu, Morteza Haghir Chehreghani, Bal\'azs Kulcs\'ar
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
arXiv:2607. 20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments.
By Giambattista Amati, Federica Mangiatordi, Simone Angelini, Emiliano Pallotti, Pierpaolo Salvo
arXiv:2604. 05175v2 Announce Type: replace-cross Abstract: We consider constrained ergodic resource optimization in wireless networks with graph-structured interference.
By Yigit Berkay Uslu, Samar Hadou, Shirin Saeedi Bidokhti, Alejandro Ribeiro
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
By Alessandro Scalese, Santhanakrishnan Narayanan, Constantinos Antoniou
MANETs enable flexible infrastructure-less wireless connectivity in dynamic and resource-constrained environments. As modern MANETs exploit multiple frequency channels and support heterogeneous traffi...
The paper introduces MANET-GNN, a message‑passing graph neural network designed to perform decentralized power allocation in dynamic, multi‑hop, multi‑channel mobile ad hoc networks (MANETs). It formulates a constrained throughput maximization problem that includes various traffic patterns such as unicast, multicast, and convergecast, and uses this as an unsupervised training objective. MANET‑GNN operates with only local, possibly noisy channel state information and a limited number of neighbor exchanges, achieving performance comparable to centralized solutions while scaling across different network topologies and sizes.
By Tomer Alter, Nir Shlezinger, Michael Segal
FedPGT introduces a progressive gradient transmission scheme for vehicular federated learning over time‑varying channels, where vehicles send high‑magnitude gradient entries according to instantaneous channel conditions. The authors derive a convergence bound showing diminishing returns governed by a power‑law decay, and formulate a stochastic optimization problem that is solved via a Lyapunov drift‑plus‑penalty approach with per‑slot surrogate variables. A low‑complexity resource allocation algorithm is proposed, and experiments on CIFAR‑10 and Argoverse demonstrate a 3.65% accuracy gain and a 12.66% reduction in displacement error compared to state‑of‑the‑art baselines.
By Jintao Yan, Tan Chen, Yuxuan Sun, Sheng Zhou, Zhisheng Niu
arXiv:2609.09708v2 Announce Type: replace-cross
Abstract: 6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast band...
By Beier Li, Mai Vu
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
The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Networks have emerged as fast, data-driven surrogates, their practical deployment is severely constrained by a spatial generalization gap.
The paper proposes an AI-native, robust network for sixth-generation (6G) communication systems, where multiple base stations serve extended reality (XR) devices. It introduces a distributed deep learning approach that jointly manages transmit power, computing power, and computation allocation while accounting for uncertainties in channel and computing states. The method uses a deep neural network trained centrally or via federated learning, injecting uncertainty samples after the DNN output to minimize worst-case delay under power and computation constraints, achieving near-centralized performance with improved privacy and scalability.
By Robert-Jeron Reifert, Hayssam Dahrouj, Alaa Alameer Ahmad, Haris Gacanin, Aydin Sezgin