IRS-Aided Secure Mobile Edge Computing for NOMA Networks

In this paper, we study an intelligent reflecting surface (IRS) assisted mobile edge computing (MEC) system based on the non-orthogonal multiple access (NOMA) technique against external eavesdroppers. In the proposed scheme, due to the limited computation ability of mobile users, the mobile users adopt the NOMA technique to offload their computation tasks to the access point (AP) assisted by the IRS. In addition, the offloading is threatened by the eavesdroppers. In order to minimize the offloading delay, we formulate an optimization problem to maximize the secure offloading rate, such that the IRS's phase shift, the offloading power and the local computation rate are optimized. To deal with the non-convex optimization problem, an alternative iterative optimization (AIO) algorithm is designed, where the semi-definite relaxation and singular value decomposition techniques are adopted to solve the non-convex items. Numerical results illustrate that the proposed scheme can improve the system computation rate and security performance compared with the current IRS assisted MEC works based on the NOMA technique.