Energy-efficient Wireless Charging and Computation Offloading In MEC Systems

TL;DR

This paper presents an integrated approach for wireless charging and computation offloading in MEC systems, optimizing energy transfer and resource allocation to enhance user device charging while minimizing energy consumption.

Contribution

It introduces a novel algorithm for joint data partitioning, power control, and energy beamforming in MEC systems to maximize wireless charging efficiency and reduce energy use.

Findings

Achieves higher wireless charging transfer compared to existing schemes.

Reduces overall energy consumption in MEC systems.

Provides an optimal resource allocation framework for simultaneous charging and offloading.

Abstract

Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) supporting multiple users requesting data computation and wireless charging. We propose an integrated solution with computation offloading to satisfy the largest proportion of requested wireless charging while keeping the energy consumption at the minimum subject to the MEC-AP transmit power and latency constraints. We propose a novel algorithm to perform data partitioning, time allocation, transmit power control and design the optimal energy beamforming for wireless charging. Our resource allocation scheme offers an energy minimizing solution compared to other schemes while also delivering higher amount of transferred charge to the users.