论文标题
在移动边缘计算中进行反向扫描数据卸载的深度加固学习
Deep Reinforcement Learning for Backscatter-Aided Data Offloading in Mobile Edge Computing
论文作者
论文摘要
由于问题大小和复杂性的增加，由于具有异质服务和资源需求的网络实体之间的紧密耦合，无线网络的优化变得非常具有挑战性。通过与环境不断互动，深入加强学习（DRL）为不同网络实体建立知识并做出自主决策以提高网络性能的机制提供了一种机制。在本文中，我们首先回顾了典型的DRL方法和最近的增强。然后，我们讨论DRL用于移动边缘计算（MEC）的应用，该应用可用于低功耗IoT设备，例如医疗保健监控中的无线传感器，以将计算工作负载卸载到附近的MEC服务器。为了平衡卸载和计算中的功耗，我们提出了一种新型的混合卸载模型，该模型利用了RF通信和低功率反向散射通信的补充操作。然后对DRL框架进行自定义，以优化两种通信技术中的传输调度和工作负载分配，与现有方案相比，该框架可显着增强卸载性能。
Wireless network optimization has been becoming very challenging as the problem size and complexity increase tremendously, due to close couplings among network entities with heterogeneous service and resource requirements. By continuously interacting with the environment, deep reinforcement learning (DRL) provides a mechanism for different network entities to build knowledge and make autonomous decisions to improve network performance. In this article, we first review typical DRL approaches and recent enhancements. We then discuss the applications of DRL for mobile edge computing (MEC), which can be used for the low-power IoT devices, e.g., wireless sensors in healthcare monitoring, to offload computation workload to nearby MEC servers. To balance power consumption in offloading and computation, we propose a novel hybrid offloading model that exploits the complement operations of RF communications and low-power backscatter communications. The DRL framework is then customized to optimize the transmission scheduling and workload allocation in two communications technologies, which is shown to enhance the offloading performance significantly compared with existing schemes.
