IRS Assisted UAV Communications against Proactive Eavesdropping in Mobile Edge Computing Networks

Abstract

In this paper, we consider mobile edge computing (MEC) networks against proactive eavesdropping. To maximize the transmission rate, IRS assisted UAV communications are applied. We take the joint design of the trajectory of UAV, the transmitting beamforming of users, and the phase shift matrix of IRS. The original problem is strong non-convex and difficult to solve. We first propose two basic modes of the proactive eavesdropper, and obtain the closed-form solution for the boundary conditions of the two modes. Then we transform the original problem into an equivalent one and propose an alternating optimization (AO) based method to obtain a local optimal solution. The convergence of the algorithm is illustrated by numerical results. Further, we propose a zero forcing (ZF) based method as sub-optimal solution, and the simulation section shows that the proposed two schemes could obtain better performance compared with traditional schemes. KEYWORDSMobile edge computing (MEC); unmanned aerial vehicle (UAV); intelligent reflecting surface (IRS); zero forcing (ZF)

A Fault-Tolerant Mobility-Aware Caching Method in Edge Computing

“…Our approach combines a multi-agent deep reinforcement learning (DRL) model with collaborative caching strategies, integrating fault tolerance mechanisms to address base station failures. In the dynamic landscape of distributed edge computing, where user and base station mobility frequently alter network conditions [19], a single-agent learning approach might struggle to keep pace with these changes. This is where the advantage of multiple DRL agents becomes evident.…”