Efficient Dynamic Beamforming Activation for UAV-Enabled Vehicular Networks

Unmanned aerial base stations (UABSs) are a promising solution for improving coverage and capacity in vehicle-to-everything (V2X) communications, particularly in dense urban areas. However, their operation is constrained by onboard energy consumption, required for both flight and communication. Beamforming, while enhancing network performance, adds to this challenge due to its energy-intensive nature. This paper proposes a sequential and hierarchical decision-making framework for UABS operations, considering trajectory planning, dynamic beamforming, and radio resource assignment (RRA). While heuristic and optimal solutions are employed for trajectory planning and RRA respectively, the beamforming model is modeled as Markov decision process (MDP) to maximize served user demand, weighted by time-varying priorities, under strict energy constraints. Leveraging a dueling double deep q-network (3DQN) algorithm that penalizes energy budget violations, an agent policy for the beamformer is then trained. Simulation results demonstrate that the proposed approach outperforms static beamforming benchmarks and closely matches an ideal step-wise oracle, achieving a balance between energy efficiency and served user demand while adapting to dynamic V2X traffic conditions.