Energy-Efficient Resource Allocation for Wireless Powered Communication Networks

• Published in: IEEE transactions on wireless communications Vol. 15; no. 3; pp. 2312 - 2327
• Main Authors: Qingqing Wu, Meixia Tao, Wing Kwan Ng, Derrick, Wen Chen, Schober, Robert
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Energy efficiency; Energy harvesting; Joints; Power control; Power generation; Resource management; Throughput; time allocation; Wireless communication; wireless powered networks; Energy efficiency; power control; wireless powered networks; time allocation
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2015.2502590

This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the nonconvexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting nonconvex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.