Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time

Abstract

Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users’ tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush–Kuhn–Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.

Clustering Technique for Mobile Edge Computing To Detect Clumps in Transportation-Related Problems

“…• To show the effectiveness of the suggested approach, statistical solutions are contrasted with those from an extensive search and other benchmark schemes. The numerical findings demonstrate that the proposed method outperforms all existing schemes and performs epsilon identically to the extensive search when task computation energy and time consumption are taken into consideration as outstanding development [3].…”

“…Sarfraz, M., Alshahrani et.al [5] this method of computing is referred to as MEC. Unfortunately, due to the compute offloading link's current shortcomings, the MEC model has not yet realised all of its promise.…”

“…Two other factors affect how long it took MEC to complete the work in total: (1) The duration of data transfer from the UEs to the MEC server, as described in (2); (2) MEC http computation. As a result, the following is how long it took to complete the assignment on the MEC server [5]:…”