面向能效优化的无人机辅助SWIPT-MEC联合资源管理：一种深度强化学习方法
Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach
AI总结：
针对偏远/灾害场景下6G物联网融合SWIPT的技术挑战，提出带定向天线的无人机辅助MEC系统，通过改进SAC算法求解双目标资源管理优化问题，实现高能效与强泛化性。
摘要：
在地面基础设施不可用的偏远地区与灾害场景中，6G物联网（IoT）网络融合同时无线信息与能量传输（SWIPT）技术面临着严峻挑战。本文提出了一种配备定向天线增强的新型无人机（UAV）辅助移动边缘计算（MEC）系统，可为地面物联网终端同时提供计算资源与能量支持。但此类系统需要制定多维度权衡策略，在无人机电池容量有限、非线性能量收集特性、任务动态到达等各类约束下，平衡无人机能耗、终端电量水平与计算资源分配。为全面应对上述挑战，本文构建了同时兼顾系统能效与终端电池可持续性的双目标优化问题；随后将这一具有混合解空间的非凸问题重构为马尔可夫决策过程（MDP），并提出一种带有动作简化机制的改进型软演员-评论家（SAC）算法，以提升其收敛性与泛化能力。仿真结果表明，所提方法在不同场景下均优于多种基线方法，可在保持高计算性能的同时实现高效能量管理；此外，该方法在不同场景（尤其是复杂环境）中展现出较强的泛化能力，验证了所设计的边界惩罚与充电奖励机制的有效性。
英文摘要：
The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
