Computation Bits Maximization in a Backscatter Assisted Wirelessly Powered MEC Network

TL;DR

This paper proposes a joint optimization scheme for a backscatter-assisted wirelessly powered MEC network, maximizing computation bits by considering practical energy harvesting models and hybrid communication methods.

Contribution

It introduces a novel optimization framework that jointly optimizes backscatter, active transmission, and local computing in a practical non-linear energy harvesting MEC system.

Findings

Proposed scheme outperforms benchmarks in weighted sum computation bits.

Derived closed-form solutions for parts of the optimization problem.

Effectiveness demonstrated through simulation results.

Abstract

In this paper, we introduce a backscatter assisted wirelessly powered mobile edge computing (MEC) network, where each edge user (EU) can offload task bits to the MEC server via hybrid harvest-then-transmit (HTT) and backscatter communications. In particular, considering a practical non-linear energy harvesting (EH) model and a partial offloading scheme at each EU, we propose a scheme to maximize the weighted sum computation bits of all the EUs by jointly optimizing the backscatter reflection coefficient and time, active transmission power and time, local computing frequency and execution time of each EU. By introducing a series of auxiliary variables and using the properties of the non-linear EH model, we transform the original non-convex problem into a convex one and derive closedform expressions for parts of the optimal solutions. Simulation results demonstrate the advantage of the proposed scheme over benchmark schemes in terms of weighted sum computation bits.