Energy-Efficient Hybrid Offloading for Backscatter-Assisted Wirelessly Powered MEC with Reconfigurable Intelligent Surfaces

TL;DR

This paper proposes an energy-efficient hybrid offloading scheme in backscatter-assisted wirelessly powered MEC networks using RIS, optimizing multiple parameters to enhance throughput and reduce energy consumption, with significant performance gains demonstrated.

Contribution

It introduces a novel joint optimization framework for energy efficiency and throughput in RIS-assisted backscatter MEC networks, including new algorithms for resource and phase shift optimization.

Findings

Energy efficiency improved by 150% with a 20-element RIS.

Proposed algorithms outperform benchmarks in system throughput and energy savings.

Closed-form solutions and advanced optimization techniques enable effective resource management.

Abstract

We investigate a wireless power transfer (WPT)-based backscatter-mobile edge computing (MEC) network with a {reconfigurable intelligent surface (RIS)}.In this network, wireless devices (WDs) offload task bits and harvest energy, and they can switch between backscatter communication (BC) and active transmission (AT) modes. We exploit the RIS to maximize energy efficiency (EE). To this end, we optimize the time/power allocations, local computing frequencies, execution times, backscattering coefficients, and RIS phase shifts.} This goal results in a multi-objective optimization problem (MOOP) with conflicting objectives. Thus, we simultaneously maximize system throughput and minimize energy consumption via the Tchebycheff method, transforming into two single-objective optimization problems (SOOPs). For throughput maximization, we exploit alternating optimization (AO) to yield two sub-problems. For the first one, we derive closed-form resource allocations. For the second one, we design the RIS phase shifts via semi-definite relaxation, a difference of convex functions programming, majorization minimization techniques, and a penalty function for enforcing a rank-one solution. For energy minimization, we derive closed-form resource allocations. We demonstrate the gains over several benchmarks. For instance, with a $20$-element RIS, EE can be as high as 3 (Mbits/Joule), a 150\% improvement over the no-RIS case (achieving only 2 (Mbits/Joule)).