Backscatter-Assisted Data Offloading in OFDMA-Based Wireless-Powered Mobile Edge Computing for IoT Networks


Nguyen P.X., Tran D.-H., Onireti O., Tin P.T., Nguyen S.Q., Chatzinotas S., Vincent Poor H.

Source title

IEEE Internet of Things Journal

Publication year

Mobile-edge computing (MEC) has emerged as a prominent technology to overcome sudden demands on computation-intensive applications of the Internet of Things (IoT) with finite processing capabilities. Nevertheless, the limited energy resources also seriously hinder IoT devices from offloading tasks that consume high power in active RF communications. Despite the development of energy harvesting (EH) techniques, the harvested energy from surrounding environments could be inadequate for power-hungry tasks. Fortunately, backscatter communications (Backcom) is an intriguing technology to narrow the gap between the power needed for communication and harvested power. Motivated by these considerations, this article investigates a backscatter-assisted data offloading in OFDMA-based wireless-powered (WP) MEC for IoT systems. Specifically, we aim at maximizing the sum computation rate by jointly optimizing the transmit power at the gateway (GW), backscatter coefficient, time-splitting (TS) ratio, and binary decision-making matrices. This problem is challenging to solve due to its nonconvexity. To find solutions, we first simplify the problem by determining the optimal values of transmit power of the GW and backscatter coefficient. Then, the original problem is decomposed into two subproblems, namely, TS ratio optimization with given offloading decision matrices and offloading decision optimization with given TS ratio. Especially, a closed-form expression for the TS ratio is obtained which greatly enhances the CPU execution time. Based on the solutions of the two subproblems, an efficient algorithm, termed the fast-efficient algorithm (FEA), is proposed by leveraging the block coordinate descent method. Then, it is compared with exhaustive search (ES), the bisection-based algorithm (BA), edge computing (EC), and local computing (LC) used as reference methods. As a result, the FEA is the best solution which results in a near-globally-optimal solution at a much lower complexity as compared to benchmark schemes. For instance, the CPU execution time of FEA is about 0.029 s in a 50-user network, which is tailored for ultralow latency applications of IoT networks.