Fairness optimization in IRS-assisted MEC computational offloading

Abstract

Nowadays, more and more multimedia services are supported by Mobile Edge Computing (MEC). However, the instability of the wireless environment brings a lot of uncertainty to the computational offloading. Additionally, intelligent reflecting surface (IRS) is considered as a potential technology to enhance Quality of Service (QoS). Therefore, in this paper, we establish a framework for IRS-assisted MEC computational offloading to solve this problem and take fairness optimization as a key point involving communication and computing resources. Minimize user consumption by optimizing bandwidth allocation, task offloading ratio, edge computing resources, transmission power and IRS phase shifts. Firstly, we decompose the problem into three aspects, such as bandwidth allocation, computing resource allocation, transmission power and IRS phase shifts. Then, an alternative optimization algorithm is proposed to find the optimum solution and its convergence is proved. Secondly, since the optimization problem on transmission power and IRS phase shifts is non-convex, we propose Riemann gradient descent (R-SGD) algorithm to solve it. Finally, numerical results show that our proposed algorithm performs better than other algorithms and achieves a superiority in the framework.