Energy Efficient Maximization for Backscatter-Assisted UAV-Powered MEC with Reconfigurable Intelligent Surface

Abstract

A system energy efficiency (EE) maximization problem is formulated in a novel backscatter-assisted unmanned aerial vehicle (UAV)-powered mobile-edge computing (MEC) system with reconfigurable intelligent surface (RIS). The reflection coefficients, computational resources, time allocation, RIS phase shifts and UAV trajectories are jointly optimized to maximize the system EE, while satisfying task constraints, energy causality constraints and trajectory constraints. The Dinkelbach-based algorithm is developed to handle the formulated problem in an alternating optimization fashion over the three sub-problems. Simulation results show that our proposed algorithm achieves superior EE compared to other benchmarked schemes.