论文标题
通过深厚的增强学习
Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning
论文作者
论文摘要
无人驾驶飞机（UAV）已被积极研究为移动的Cloudlets，以提供应用程序卸载机会并提高用户设备（UES）的安全水平。在此通信中，我们提出了一个混合无人机的安全卸载系统，在该系统中，无人机通过切换堵塞和中继之间的模式来充当助手，以最大程度地提高UES的保密总和。这项工作旨在优化（i）助手无人机的轨迹，（ii）模式选择策略以及（iii）在卸载成就的限制和无人机的操作限制下，UES的卸载决策。该解决方案是通过基于深层的确定性策略梯度（DDPG）的方法提供的，该方法通过数值模拟验证了其出色的性能，并将其与传统方法进行了比较。
Unmanned aerial vehicles (UAVs) have been actively studied as moving cloudlets to provide application offloading opportunities and to enhance the security level of user equipments (UEs). In this correspondence, we propose a hybrid UAV-aided secure offloading system in which a UAV serves as a helper by switching the mode between jamming and relaying to maximize the secrecy sum-rate of UEs. This work aims to optimize (i) the trajectory of the helper UAV, (ii) the mode selection strategy and (iii) the UEs' offloading decisions under the constraints of offloading accomplishment and the UAV's operational limitations. The solution is provided via a deep deterministic policy gradient (DDPG)-based method, whose superior performance is verified via a numerical simulation and compared to those of traditional approaches.
