Resource and Trajectory Optimization for Secure Enhancement in IRS-Assisted AAV-MEC Systems

Abstract

Intelligent reflecting surface (IRS)-assisted autonomous aerial vehicle-mobile edge computing (AAV-MEC) systems have brought great advancements in future networks. However, due to the line-of-sight transmission, confidential task information is highly susceptible to interception by malicious AAV eavesdroppers, posing a significant security challenge. To overcome this challenge, we aim to design a joint resource and trajectory optimization scheme to enhance the secure performance of the IRS-assisted AAV-MEC system. Specifically, the minimum secrecy capacity of users is maximized by optimizing time allocation, transmit power, local computation CPU frequency, IRS phase shifts, and AAV trajectory while satisfying users' task processing capacity requirements. To address the non-convex optimization problem, we first employ mathematical techniques to simplify it into a more tractable form, and subsequently decompose it into several sub-problems. These sub-problems can be solved iteratively through combining phase alignment with successive convex approximation. Simulation results verify that our proposed scheme can improve the secrecy capacity performance of the considered IRS-assisted AAV-MEC system compared with benchmarks.