Vehicular Networks

Latest papers

8 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Graph Neural Networks for Resource Allocation in Integrated Sensing and Communication
Nov 5, 2026Junyu Liao, Lili Chen, Tian Han +1Vehicular Networks Wireless Resource Allocation
Department of Electrical and Electronic Engineering, University of Melbourne, Australia · Department of Electrical and Computer Systems Engineering, Monash University, Australia
Communication-Efficient Private Federated Reinforcement Learning with Post-Quantum Authentication for Vehicular Networks
Oct 29, 2026Hojat Allah Salehi, Devrim Aras, Md Jueal Mia +4Vehicular Networks Federated RL
Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA · Security, Optimization, and Learning for Interdependent Networks Lab (solid lab), Miami, FL 33199, USA · Department of Computer Science, The University of Texas at Dallas, Richardson, TX 75080, USA +1
One-Shot Federated Model Editing for Device Dynamics at the Edge
Oct 26, 2026Youming Tao, Weijun Yin, Huanyi Xie +5Vehicular Networks Federated Learning
School of Electrical Engineering and Computer Science, TU Berlin, 10623 Berlin, Germany · School of Cyber Science and Technology, Beihang University, Beijing 100191, China · Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100083, China +2
Clean-label Poisoning Attack and Defense Game for Federated Learning in Vehicular Networks
Oct 23, 2026Yalin E. Sagduyu, Tugba Erpek, Nathan Stephenson +1Vehicular Networks Federated Learning
Nexcepta Inc., Gaithersburg, MD, USA · RTX BBN Technologies, Cambridge, MA, USA
Trust as a Field: A Macroscopic Representation for Vehicular Networks
Aug 18, 2026Md Mahmudul Islam, Shaurya AgarwalVehicular Networks Intelligent Transportation Systems
Dept. of Civil, Environmental, and Construction Eng. University of Central Florida Orlando, USA
Federated Foundation Models over Vehicular Networks
Jun 5, 2026Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali +5Multimodal Federated Learning Vehicular Networks
Network-Efficient World Model Token Streaming
May 11, 2026Shatadal Mishra, Ahmadreza Moradipari, Nejib AmmarVehicular Networks Intelligent Transportation Systems
InfoTech Labs, Toyota Motor North America R&D, Mountain View, CA, USA
Deep Adaptive Rate Allocation in Volatile Heterogeneous Wireless Networks
Mar 21, 2026Gregorio Maglione, Veselin Rakocevic, Markus Amend +1Vehicular Networks Wireless Resource Allocation
Department of Engineering, City St George’s, University of London, London EC1V 0HB, United Kingdom · Deutsche Telekom, Darmstadt, Germany

Nov 5, 2026J/K move · Enter open · S save

Graph Neural Networks for Resource Allocation in Integrated Sensing and Communication

Junyu Liao, Lili Chen, Tian Han, Jamie Evans

Department of Electrical and Electronic Engineering, University of Melbourne, Australia · Department of Electrical and Computer Systems Engineering, Monash University, Australia

Vehicular Networks Wireless Resource Allocation Integrated Sensing and Communication Graph Neural Networks Intelligent Transportation Systems

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In vehicular integrated sensing and communication (ISAC) networks, each service-provider vehicle uses a limited power budget to serve multiple sensing targets and communication receivers. Maximizing the communication sum-rate subject to sensing signal-to-interference-plus-noise ratio (SINR) constraints is nonconvex, and iterative optimization is costly to rerun for every network layout. This paper proposes a graph neural network (GNN)-based power allocation algorithm, termed GNN-ISAC. Concretely, GNN-ISAC addresses the aforementioned problem by considering two distinct edge types in the graph representation, one capturing the inter-provider interference, the other representing the intra-provider power-budget sharing. GNN-ISAC is trained without labels using a sensing-penalized sum-rate loss. We also adapt the weighted mean-square error minimization (WMMSE) algorithm to this problem by introducing a sensing-power reservation step. Simulation results show that GNN-ISAC achieves communication sum-rates close to WMMSE with low sensing-link violation rates and much less computation. It also outperforms an existing heterogeneous GNN and a channel-blind baseline, and generalizes to larger networks without retraining.