Secure intelligent reflecting surface assisted mobile edge computing system with wireless power transfer

In this paper, we study an Intelligent Reflecting Surface (IRS) assisted Mobile Edge Computing (MEC) system under eavesdropping threats, where the IRS is used to enhance the energy signal transmission and the offloading performance between Wireless Devices (WDs) and the Access Point (AP). Specifically, in the proposed scheme, the AP first powers all WDs with the wireless power transfer through both direct and IRS-assisted links. Then, powered by the harvested energy, all WDs securely offload their computation tasks through the two links in the time division multiple access mode. To determine the local and offloading computational bits, we formulate an optimization problem to jointly design the IRS's phase shift and allocate the time slots constrained by the security and energy requirements. To cope with this non-convex optimization problem, we adopt semidefinite relaxations, singular value decomposition techniques, and Lagrange dual method. Moreover, we propose a dichotomy particle swarm algorithm based on the bisection method to process the overall optimization problem and improve the convergence speed. The numerical results illustrate that the proposed scheme can boost the performance of MEC and secure computation rates compared with other IRS-assisted MEC benchmark schemes.