Utility Maximization for IRS Assisted Wireless Powered Mobile Edge Computing and Caching (WP-MECC) Networks

Abstract

This paper exploits an intelligent reflecting surface (IRS) assisted wireless powered mobile edge computing and caching (WP-MECC) network. In particular, an IRS is utilized to reflect energy signals from a power station (PS) to various IoT devices for energy harvesting during uplink wireless energy transfer (WET). These devices collect energy to support their own partially local computing for computational tasks and their offloading capabilities to an access point (AP), with the help of IRS via time or frequency division multiple access (TDMA or FDMA). The AP is equipped with a local cache connected with a MEC server via a backhaul link, which prefetches the data to facilitate edge computing capabilities. The maximization of a utility function is formulated to evaluate the overall network performance, which is defined as the difference between the sum of computational bits (offloading bits and local computing bits) and total backhaul cost. Due to multiple coupled variables, we first design the optimal caching strategy. Then, an auxiliary vector is introduced to coordinate the energy consumption of local computing and offloading, where its optimal solution can be achieved by an exhaustive search. Moreover, we utilize the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive the optimal time scheduling for the TDMA scheme or the optimal bandwidth allocation for the FDMA counterpart in closed form. The IRS phase shifts are iteratively designed by employing the quadratic transformation (QT) and the Riemannian Manifold Optimization (RMO). Finally, simulation results are demonstrated to validate the network utility performance and confirm the advantage of the employment of IRS, the optimal IRS phase shift design and caching strategy, in comparison to the benchmark schemes.Index Terms-Intelligent reflecting surface (IRS), wireless powered mobile edge computing and caching (WP-MECC), utility

STAR-RIS-Aided Mobile Edge Computing: Computation Rate Maximization With Binary Amplitude Coefficients

“…Partial offloading is designed to cope with computation tasks with data partition, in which tasks can be arbitrarily divided to facilitate parallel operations at users for local computing and offloading to the APs for edge computing [16]. We use time division duplex protocol and adjust RIS phase shifts between uplink and downlink to reconfigure the propagation environment in real-time via a control link between RIS and APs [17]. To guarantee the quality of services provided to users, it is assumed that multiple APs in the uplink transmission can successfully receive each user's data, enabling computation repetition at different edge servers and thus creating multiple copies of the computation results at different APs [18].…”

An Efficient Block Successive Upper-Bound Minimization Algorithm for Caching a Reconfigurable Intelligent Surface-Assisted Downlink Non-Orthogonal Multiple Access System

“…With regard to the assistance of RIS to MEC, there are recently a few works that consider the RIS deployment to enhance caching communications [23][24][25]. For instance, in order to optimally allocate the cache resource for BS and design the beamforming vectors for RIS, authors have formulated corresponding problems and obtained the sub-optimal solutions by individually searching algorithms for each optimization parameter [23].…”

“…RISassisted communication was considered in a wireless-powered caching system, where the optimization problem was formulated as a Stackelberg game process and solved by the sub-optimal approach of alternating optimization [24]. Moreover, in order to maximize the ratio of locally cached data over the computation requirement, RIS was utilized to assist the edge caching and computing for a wireless powered mobile network, in which an exhaustive search was performed to find the optimal energy allocation after designing the caching placement and Lagrange dual method was applied to solve the optimization problem [25].…”