Resource Allocation in Backscatter-Assisted Wireless Powered MEC Networks With Limited MEC Computation Capacity

• Published in: IEEE transactions on wireless communications Vol. 21; no. 12; pp. 10678 - 10694
• Main Authors: Ye, Yinghui, Shi, Liqin, Chu, Xiaoli, Hu, Rose Qingyang, Lu, Guangyue
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Backscatter communications; Backscattering; computation bits; computation energy efficiency; Computation offloading; Edge computing; Energy; Energy harvesting; Internet of Things; Iterative algorithms; Iterative methods; Maximization; Mobile computing; mobile edge computing; Nodes; Nonlinear programming; Optimization; Radio spectrum management; Reflectance; Resource allocation; Resource management; Servers; Task analysis; Time-frequency analysis; Wireless communication; Wireless networks
• DOI: 10.1109/TWC.2022.3185825
• ISSN: 1536-1276

In this paper, we consider a backscatter-assisted wireless powered mobile edge computing (MEC) network, where multiple Internet-of-Things (IoT) nodes harvest energy from the energy signals transmitted by a power beacon (PB) and utilize the harvested energy for local computing and task offloading via hybrid backscatter communication (BackCom) and active transmission (AT). Considering the limited computation capacity of the MEC server and the quality-of-service (QoS) and energy-causality constraints per IoT node, we propose two resource allocation schemes to maximize the total computation bits of all the IoT nodes and the system computation energy efficiency (EE), respectively, by jointly optimizing the computation frequency and time of the MEC server and each IoT node, the transmit power of the PB and each IoT node, and the BackCom power reflection coefficient and the time for energy harvesting (EH), BackCom, and AT of each IoT node. The non-convex computation bits maximization problem is transformed to a convex one by introducing a series of auxiliary variables and proof by contradiction, and then solved by the existing convex tools. The system computation EE maximization is a non-convex nonlinear programming problem. We propose a two-layer iterative algorithm to solve it optimally and devise a reduced-complexity iterative algorithm to solve it sub-optimally by leveraging the block coordinate decent technique. Computer simulations validate the convergence of the proposed iterative algorithms and their superior performance over the benchmark schemes in terms of the computation bits or EE.