Fairness-Aware Resource Allocation for Timely Information Dissemination in C-V2X Networks

With the rapid development of intelligent transportation systems, the increasing number of connected vehicles and onboard sensing devices has generated substantial data traffic and placed stringent demands on communication resources in the Internet of Vehicles. In three-dimensional urban environments, physical blockage, high mobility, and co-channel interference can cause temporary link disruptions, preventing the timely dissemination of safety-critical vehicular information. This study investigates joint resource allocation for Cellular Vehicle-to-Everything (C-V2X) networks with heterogeneous vehicular platoons. We propose an Age of Information (AoI)- and Fairness-Aware Multi-Agent Resource Allocation (AF-MARL) 1 under imperfect and delayed channel state information. Its novelty lies in combining an AoI-dependent power cost with a cross-platoon AoI-dispersion penalty within a multi-task multi-agent reinforcement learning (MARL) architecture, thereby reducing unnecessary high-power transmission by favorable platoons while explicitly protecting disadvantaged platoons without adding Actor or Critic networks. Simulation results show that AF-MARL reduces interference and persistent AoI outages while maintaining reliable Cooperative Awareness Message (CAM) delivery under different platoon spacings and sizes.