Joint Angle-Based User Selection and Multiagent Reinforcement Learning for Dynamic Beamforming in HAPS-Assisted IoT Vehicular Networks

High Altitude Platform Stations (HAPS) have emerged as a compelling solution to extend wireless coverage for next-generation vehicular networks, thanks to their ability to hover at altitudes of around 20 km and provide robust Line-Of-Sight (LoS) links. However, the expansive coverage area of a single HAPS introduces intense co-channel interference among vehicles, particularly when traffic is dense or nonuniformly distributed. To address these challenges, this article proposes a joint angle-based user selection (AUS) strategy and an attention-based mean-field actor-critic (MF-A2C) framework for fully digital beamforming. First, AUS partitions angle-near vehicles into different groups to reduce within-group correlation. Then, each group is served by a high-dimensional beamforming vector, where an attention-based mean-field mechanism captures intergroup interference without incurring exponential complexity. Extensive simulations under realistic highway and urban mobility models demonstrate that the proposed scheme not only accelerates the convergence of the reinforcement learning process but also improves system throughput.