Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning

TL;DR

This paper introduces a hybrid UAV system that dynamically switches between jamming and relaying modes, using deep reinforcement learning to optimize secure offloading and improve secrecy rates in wireless networks.

Contribution

It proposes a novel hybrid UAV-assisted offloading framework with mode switching, optimized through deep reinforcement learning for enhanced security and efficiency.

Findings

The DDPG-based method outperforms traditional approaches in secrecy sum-rate.

Optimized UAV trajectory and mode selection improve offloading security.

Numerical simulations validate the effectiveness of the proposed system.

Abstract

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.