In this paper, we propose an intelligent reflecting surface (IRS)-enabled low-altitude multi-access edge computing (MEC) architecture, where an aerial MEC server cooperates with a terrestrial MEC server to provide computing services, while hybrid IRSs (i.e., building-installed and UAV-carried IRSs) are deployed to enhance the air-ground connectivity under blockage. Based on this architecture, we formulate a multi-objective optimization problem (MOOP) to minimize the task completion delay and energy consumption by jointly optimizing task offloading, UAV trajectory control, IRS phase-shift configuration, and computation resource allocation. The considered problem is NP-hard, and thus we propose a hierarchical online optimization approach (HOOA) to efficiently solve the problem. Specifically, we reformulate the MOOP as a Stackelberg game, where MEC servers collectively act as the leader to determine the system-level decisions, while the vehicles act as followers to make individual decisions. At the follower level, we present a many-to-one matching mechanism to generate feasible discrete decisions. At the leader level, we propose a generative diffusion model-enhanced twin delayed deep deterministic policy gradient (GDMTD3) algorithm integrated with a Karush-Kuhn-Tucker (KKT)-based method, which is a deep reinforcement learning (DRL)-based approach, to determine the continuous decisions. Simulation results demonstrate that the proposed HOOA achieves significant improvements, which reduces average task completion delay by 2.5% and average energy consumption by 3.1% compared with the best-performing benchmark approach and state-of-the-art DRL algorithm, respectively. Moreover, the proposed HOOA exhibits superior convergence stability while maintaining strong robustness and scalability in dynamic environments.

翻译：本文提出一种智能反射面（IRS）使能的低空多接入边缘计算（MEC）架构，其中空中MEC服务器与地面MEC服务器协同提供计算服务，同时部署混合IRS（即建筑安装与无人机搭载的IRS）以增强遮挡条件下的空-地连接性。基于此架构，我们构建了一个多目标优化问题（MOOP），通过联合优化任务卸载、无人机轨迹控制、IRS相移配置及计算资源分配，以最小化任务完成时延与能耗。该问题属于NP难问题，因此我们提出一种分层在线优化方法（HOOA）来高效求解。具体而言，我们将MOOP重构为一个Stackelberg博弈，其中MEC服务器共同作为领导者确定系统级决策，而车辆作为跟随者做出个体决策。在跟随者层级，我们提出一种多对一匹配机制以生成可行的离散决策。在领导者层级，我们提出一种生成式扩散模型增强的双延迟深度确定性策略梯度（GDMTD3）算法，并结合基于Karush-Kuhn-Tucker（KKT）条件的方法——这是一种基于深度强化学习（DRL）的方法——来确定连续决策。仿真结果表明，所提出的HOOA取得了显著性能提升：与最佳基准方法和最先进的DRL算法相比，分别将平均任务完成时延降低了2.5%，平均能耗降低了3.1%。此外，所提HOOA在动态环境中展现出优异的收敛稳定性，同时保持了强大的鲁棒性与可扩展性。