Computational EE Fairness in Backscatter-Assisted Wireless Powered MEC Networks

In this letter, we study the computational energy efficiency (EE) fairness in a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple edge users (EUs) can offload tasks to the MEC server via passive backscatter communications (BackComs) and active transmissions (ATs) under the guidance of the harvest-then-transfer protocol. Specifically, with the practical non-linear energy harvesting (EH) model and the partial offloading scheme considered at each EU, we propose a max-min computational EE-based resource allocation scheme to ensure the fairness among multiple EUs by jointly optimizing the reflection coefficient, transmit power, local computing frequency and execution time of each EU, as well as EUs' time sharing between BackComs and ATs, and then develop a Dinkelbach-based iterative algorithm to obtain the optimal solutions. We further employ the Lagrange duality method to obtain instrumental insights on the max-min computational EE-based resource allocation scheme. Simulation results verify the superiority of the proposed scheme over benchmark schemes in terms of computational EE fairness.