Revenue-maximization-oriented access strategy for UAV-assisted mmWave user-centric multi-UAV access networks

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) access networks can provide flexible wireless service provisioning in dense and dynamic user equipment (UE) environments. To meet the flexible access requirements of dense and dynamic UE environments, this paper considers a UAV-assisted mmWave user-centric multi-UAV access network, where each UE can be served by multiple UAV access points without being restricted by fixed cell boundaries. In such networks, revenue-oriented access control is challenging because UE association, sub-channel allocation, and transmit-power control are coupled with service quality, resource pricing, residual interference, and UAV operation costs. This paper investigates a revenue-maximization-oriented joint access and resource allocation problem for the considered UAV-assisted mmWave user-centric multi-UAV access network. A revenue-aware utility model is formulated by jointly considering quality-of-service (QoS) tiered service revenue, sub-channel cost, transmission-power cost, active-UAV operation cost, and residual co-channel interference. To obtain an effective solution, the formulated mixed-integer, non-convex, and nonlinear programming problem is decomposed into three subproblems, namely UE association, sub-channel allocation, and power control. Accordingly, a revenue-maximization-oriented access strategy (RMOAS) is developed by integrating circular matching-based UE association, Hungarian matching-based sub-channel allocation, and interior-point-based power control. Simulations across 20 independent random seeds show that RMOAS achieves higher operator utility than heuristic and learning-based baselines. In the main scenario, RMOAS achieves a mean utility of 3645.91 ± 28.83 at the 95% confidence level, outperforming deep deterministic policy gradient (DDPG), immune optimization access strategy (IOAS), many-to-one matching access strategy (MMAS), nearest-distance matching access strategy (NMAS), and random allocation access strategy (RAAS). Ablation results further demonstrate the individual contributions of the UE association, sub-channel allocation, and power-control components.