Proximal Policy Optimization-Based Hierarchical Decision-Making Mechanism for Resource Allocation Optimization in UAV Networks

To address the resource allocation problem in dynamic environments where multiple unmanned aerial vehicle base stations (UAV-BSs) provide efficient downlink services to ground users, this paper proposes a novel hierarchical decision-making mechanism based on the Proximal Policy Optimization (PPO) algorithm. The proposed method optimizes time-frequency resource allocation in the downlink, aiming to maximize the total user throughput over multiple time slots. By constructing channel and interference models, the complex multi-channel resource allocation problem is decomposed into a series of single-channel decision subproblems, significantly reducing the action space complexity. Specifically, the original exponential complexity O(NM) (where N is the number of users and M is the number of channels) is reduced to a linear complexity O(N), effectively alleviating the curse of dimensionality. Simulation results demonstrate that the proposed hierarchical architecture, integrated with the PPO algorithm, achieves superior performance in terms of total throughput, convergence speed, and stability compared to existing methods. This study provides new insights and technical support for efficient resource management in UAV-BS systems operating in complex and dynamic environments.