论文标题
AI驱动的异质MEC系统，为动态环境提供了无人机协助 - 挑战和解决方案
AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment -- Challenges and Solutions
论文作者
论文摘要
通过充分利用网络边缘的计算，通信和缓存（3C）资源，移动边缘计算（MEC）被设想为下一代网络的关键推动因素之一。但是，当前的固定安装MEC体系结构可能无法在动态环境中做出实时决定，尤其是在大规模的情况下。为了解决这个问题，在本文中，提出了一个异质的MEC（H-MEC）架构，该结构由固定单元组成，即地面站（GSS）以及移动节点，即地面车辆（GVS）和无人驾驶空中汽车（UAV）（UAVS），所有这些都具有3C。讨论了H-MEC的主要挑战，即移动边缘节点管理，实时决策，用户协会和资源分配以及可能的基于人工智能（AI）的解决方案。此外，提出了基于AI的联合资源调度（是）具有两种基于AI的机制的框架，即基于深的神经网络（DNN）基于深度的和深的增强学习（DRL）基于基于的建筑。基于在线增量学习的基于DNN的解决方案应用了全球优化器，因此比在线政策更新的基于DRL的体系结构具有更好的性能，但需要更长的培训时间。给出了仿真结果以验证我们提出的效率是框架。
By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for the next generation networks. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, i.e., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed. In addition, the AI-based joint Resource schEduling (ARE) framework with two different AI-based mechanisms, i.e., Deep neural network (DNN)-based and deep reinforcement learning (DRL)-based architectures are proposed. DNN-based solution with online incremental learning applies the global optimizer and therefore has better performance than the DRL-based architecture with online policy updating, but requires longer training time. The simulation results are given to verify the efficiency of our proposed ARE framework.
