基于深度渐进强化学习的IRS与UAV辅助MEC系统灵活资源调度框架
Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
AI总结：
本文提出FRES框架，采用深度渐进强化学习联合优化无人机位置、IRS相移、任务卸载与资源分配，以最小化MEC系统能耗，并支持无人机数量动态变化下的实时最优调度。
摘要：
智能反射面（IRS）和无人机（UAV）辅助的移动边缘计算（MEC）系统被广泛应用于临时和应急场景。我们的目标是通过在无人机数量可变的情况下联合优化无人机位置、IRS相移、任务卸载和资源分配，以最小化MEC系统的能耗。为此，我们提出了一种灵活资源调度（FRES）框架，采用一种新颖的深度渐进强化学习方法，包含以下创新：首先，提出了一种新颖的多任务智能体来处理混合整数非线性规划（MINLP）问题。该多任务智能体具有两个为不同任务设计的输出头，其中分类头用于做出具有整数变量的卸载决策，而拟合头用于求解具有连续变量的资源分配。其次，引入了一种渐进调度器，通过逐步调整智能体中的部分神经元，使智能体适应无人机数量的变化。这种结构可以自然地积累经验，并避免灾难性遗忘。最后，引入了一种轻量禁忌搜索（LTS）以增强FRES的全局搜索能力。数值结果表明了FRES框架的优越性，即使在动态MEC系统中也能实现实时和最优的资源调度。
英文摘要：
The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems.
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。
