Energy-Efficient Resource Allocation for Backscatter-Assisted Wireless Powered Communication Networks in Twin Workshop

• Published in: Wireless communications and mobile computing 2022, Vol.2022, p.1-10
• Main Authors: Li, Yujian, Zhang, Xinxing
• Format: Article
• Language: English
• Subjects: Backscattering; Communication networks; Energy efficiency; Energy shortages; Equivalence; Integer programming; Internet of Things; Iterative algorithms; Mixed integer; Optimization; Resource allocation; Sensors; Subtraction; Wireless communications; Wireless networks; Workshops
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/6144741

The problem of energy shortage in sensor nodes caused by frequent data interactions is one of the major constraints on the development of twin workshops. A backscatter-assisted wireless powered communication network (BAWPCN) has been deemed a potential solution for addressing the problem of energy shortage in twin workshops. How to effectively ensure the link energy efficiency (EE) while satisfying the quality of servers for each user has been of high interest, while it has not been well studied in previous works. Inspired by this, in this paper, we propose a resource allocation scheme based on the max–min criterion, considering the user quality of service and energy-causality constraints. The optimization problem is formulated as a mixed-integer nonconvex fractional planning problem which is aimed at maximizing the minimum EE of each link. The generalized fractional theory is used to transform the nonconvex fractional planning problem into an equivalent mixed-integer nonconvex subtraction optimization problem, and then, the mixed-integer nonconvex subtractive optimization problem is transformed into an equivalent nonconvex optimization problem by introducing relaxation variables to eliminate the integer programming problem arising from the maximum-minimum function. Based on this, the block coordinate descent method is used to decompose the transformation problem into two convex subproblems, and an iterative algorithm is proposed to solve the transformation problem. Simulation results verify the fast convergence of the proposed iterative algorithm and show that the proposed resource allocation method can effectively guarantee the fairness of the energy efficiency of the system in twin workshops.