AI 中文总结
研究无人机支持的MEC中保证SLA稳定性的问题，提出预测性多智能体强化学习框架，通过协调轨迹控制和资源分配维持SLA稳定，设计奖励函数，用MAPPO训练智能体，模拟表明该框架能显著提升SLA稳定性且保持性能优势。
AI 中文摘要
无人机支持的移动边缘计算（MEC）为异构网络切片提供了灵活的容量供应。然而，在动态用户移动性、随机任务到达以及机载能量和计算资源受限的情况下，保证切片级服务水平协议（SLA）仍然是一个基本挑战。本文提出了一种预测性多智能体强化学习框架，通过协调轨迹控制和计算资源分配，在无人机支持的MEC中主动维持SLA稳定性。一个轻量级预测模块预测近期用户移动性，使无人机能够在SLA违规发生前预测拥塞并重新定位。设计了一个SLA感知奖励函数，明确惩罚切片间的违规概率和持续时间以及总能耗。无人机智能体使用具有集中训练和分散执行的多智能体近端策略优化（MAPPO）进行训练，实现可扩展的在线决策。具有实际移动轨迹的事件驱动模拟表明，与基线相比，所提出的框架显著提高了SLA稳定性，同时保持了有竞争力的能源效率和延迟性能，在具有足够准确预测信息的情况下接近最优性能。
英文摘要
Unmanned Aerial Vehicle (UAV)-enabled Mobile Edge Computing (MEC) offers flexible capacity provisioning for heterogeneous network slices, including Hyper-Reliable and Low-Latency Communication (HRLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). However, guaranteeing slice-level Service-Level Agreements (SLAs) under dynamic user mobility, stochastic task arrivals, and constrained onboard energy and computing resources remains a fundamental challenge. This paper proposes a predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation. A lightweight prediction module forecasts near-future user mobility, enabling UAVs to anticipate congestion and reposition before SLA violations occur. We design an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices, alongside total energy consumption. UAV agents are trained using Multi-Agent Proximal Policy Optimization (MAPPO) with centralized training and decentralized execution, enabling scalable online decision-making. Event-driven simulations with realistic mobility traces demonstrate that the proposed framework significantly improves SLA stability compared with baselines while maintaining competitive energy efficiency and delay performance, approaching oracle-level performance with sufficiently accurate predictive information.
