The Low-Altitude Economy (LAE) is rapidly expanding, giving rise to low-altitude wireless networks (LAWNs), where large-scale cellular-connected unmanned aerial vehicle (UAV) deployments support heterogeneous mission-critical applications over multi-cell ground base station (GBS) infrastructures. To ensure mission success, each UAV must jointly optimize communication throughput and mission completion efficiency. In fifth-generation (5G) new radio (NR) systems, the equal resource block (RB) allocation policy induces strong strategic coupling among UAV trajectories: when a UAV enters a GBS cell, it reduces the RB share available to all co-served UAVs, thereby altering their achievable rates and trajectory incentives through shared wireless resources. Existing studies either ignore this coupling or focus on single-cell infrastructure, leaving the multi-cell, congestion-aware UAV trajectory planning problem insufficiently addressed. To fill this gap, we formulate the problem as a cooperative stochastic congestion game with a communication-and-mission-aware utility function, and propose a centralized-training decentralized-execution multi-agent proximal policy optimization (CTDE-MAPPO) algorithm to maximize social welfare under multi-cell RB congestion. Simulation results show that the proposed method outperforms QMIX, independent Q-learning, and random baselines in terms of aggregate utility and mission success rate, while achieving stable convergence within practical training budgets.

翻译：低空经济（LAE）正在快速发展，催生了低空无线网络（LAWN）。在该网络中，大规模蜂窝连接的无人飞行器（UAV）在多小区地面基站（GBS）基础设施上支持异构任务关键型应用。为确保任务成功，每架UAV必须联合优化通信吞吐量与任务完成效率。在第五代（5G）新空口（NR）系统中，等资源块（RB）分配策略在UAV轨迹间引入了强策略耦合：当UAV进入某GBS小区时，会降低同小区所有UAV共享RB的份额，从而通过共享无线资源改变其可达速率与轨迹激励。现有研究要么忽略这种耦合，要么仅聚焦于单小区基础设施，导致多小区环境下拥堵感知的UAV轨迹规划问题尚未得到充分解决。为填补这一空白，我们将该问题建模为具有通信与任务感知效用函数的合作式随机拥堵博弈，并提出一种集中训练-分散执行的多智能体近端策略优化（CTDE-MAPPO）算法，以在多小区RB拥堵条件下最大化社会福利。仿真结果表明，所提方法在总效用和任务成功率上优于QMIX、独立Q学习及随机基线方法，同时能在实际训练预算内实现稳定收敛。