Computational Energy Efficiency Optimization for IRS-Assisted MEC System: Active or Passive?

Active intelligent reflecting surfaces (AIRS) effectively overcome the multiplicative path loss effects encountered by passive intelligent reflecting surfaces (PIRS). To unleash the full potential of MEC systems, this paper investigates AIRS-assisted MEC systems in a multi-user scenario. Enhancing energy efficiency has emerged as a primary goal of 6G networks. Although AIRS has been shown to greatly improve the computational rate of MEC systems, its impact on computational energy efficiency (CEE) has not been fully studied. Therefore, this study aims to maximize CEE as the optimization objective, posing a challenging non-convex fractional programming (FP) problem. To address this issue, we propose an iterative algorithm based on the Dinkel-bach method. We first transform the initial problem into a convex optimization problem using the FP method. We then employ the block coordinate descent (BCD) method to decouple the reformulated problem and derive closed-form optimal solutions for each subproblem through Lagrangian dual transformation and KKT conditions. Simulation results validate the effectiveness of the proposed method, and elucidate the preferable operating points for choosing between AIRS and PIRS in terms of CEE.