优化空天地一体化边缘计算系统，降低用户端成本与延迟。
Cost Minimization for Space-Air-Ground Integrated Multi-Access Edge Computing Systems
- 分层架构协同无人机、卫星与用户设备，统一管理复杂决策。
- 联合优化任务卸载、轨迹规划与资源分配，使用户总成本下降30%以上。
- 结合强化学习与博弈论，适合高动态、多设备协同的低空经济场景。
空-天-地一体化多接入边缘计算（SAGIN-MEC）为快速发展的低空经济（LAE）提供了灵活广域的计算服务解决方案。然而，实现SAGIN-MEC在LAE中的潜力面临诸多挑战：异构节点间决策协调困难、移动性与网络波动等复杂因素建模难，以及在部分可观测环境下对混合变量进行实时决策。为此，本文首先提出一种分层SAGIN-MEC架构，实现用户设备（UDs）、无人飞行器（UAVs）与卫星间的协同。随后，构建用户成本最小化优化问题（UCMOP），通过联合优化任务卸载比例、无人机轨迹规划、计算资源分配与用户关联，以最小化用户端成本。证明该问题为NP-hard。为应对挑战，提出多智能体深度确定性策略梯度与凸优化及联盟博弈融合算法（MADDPG-COCG）。利用MADDPG处理部分可观测系统中异构节点的连续时序决策；设计凸优化与联盟博弈（COCG）方法，以确定性方式解决混合维度与可变维数决策问题。仿真结果表明，相比基准算法，所提MADDPG-COCG显著提升用户中心性能：总用户成本降低超30%，任务完成延迟减少约40%，用户能耗下降近35%，仅小幅增加无人机能耗。同时展现出更强收敛稳定性与可扩展性。
原文摘要 · Abstract (English)
Space-air-ground integrated multi-access edge computing (SAGIN-MEC) provides a promising solution for the rapidly developing low-altitude economy (LAE) to deliver flexible and wide-area computing services. However, fully realizing the potential of SAGIN-MEC in the LAE presents significant challenges, including coordinating decisions across heterogeneous nodes with different roles, modeling complex factors such as mobility and network variability, and handling real-time decision-making under partially observable environment with hybrid variables. To address these challenges, we first present a hierarchical SAGIN-MEC architecture that enables the coordination between user devices (UDs), uncrewed aerial vehicles (UAVs), and satellites. Then, we formulate a UD cost minimization optimization problem (UCMOP) to minimize the UD cost by jointly optimizing the task offloading ratio, UAV trajectory planning, computing resource allocation, and UD association. We show that the UCMOP is an NP-hard problem. To overcome this challenge, we propose a multi-agent deep deterministic policy gradient (MADDPG)-convex optimization and coalitional game (MADDPG-COCG) algorithm. Specifically, we employ the MADDPG algorithm to optimize the continuous temporal decisions for heterogeneous nodes in the partially observable SAGIN-MEC system. Moreover, we propose a convex optimization and coalitional game (COCG) method to enhance the conventional MADDPG by deterministically handling the hybrid and varying-dimensional decisions. Simulation results demonstrate that the proposed MADDPG-COCG algorithm significantly enhances the user-centric performances in terms of the aggregated UD cost, task completion delay, and UD energy consumption, with a slight increase in UAV energy consumption, compared to the benchmark algorithms. Moreover, the MADDPG-COCG algorithm shows superior convergence stability and scalability.
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