Full-Duplex Wireless-Powered Communication Network With Energy Causality

• Published in: IEEE transactions on wireless communications Vol. 14; no. 10; pp. 5539 - 5551
• Main Authors: Kang, Xin, Ho, Chin Keong, Sun, Sumei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access to information; Communication networks; Communications networks; Convex Optimization; Downlink; Energy harvesting; Energy transmission; Information processing; Mathematical models; Optimal Time Allocation; Optimization; Receiving; Resource management; Scheduling; Time Division Multiple Access; Uplink; Wireless communication; Wireless Power Transfer; Wireless sensor networks; convex optimization; optimal time allocation; wireless power transfer; Energy harvesting
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2015.2439673

In this paper, we consider a wireless communication network with a full-duplex hybrid energy and information access point and a set of wireless users with energy harvesting capabilities. The hybrid access point (HAP) implements full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and the other for simultaneously receiving information from the users via time division multiple access (TDMA) in the uplink. Each user can continuously harvest wireless power from the HAP until it transmits, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for the current transmission. This leads to the causal dependence of each user's harvesting time on the transmission time of earlier users, e.g., the second user scheduled to transmit can harvest more energy if the first user has longer transmission time. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed full-duplex wireless-powered communication network. For the STM problem, the optimal solution is obtained as a closed-form expression, which can be computed with linear complexity. For the TTM problem, by exploiting the properties of the coupled constraints, we propose a two-step algorithm to obtain an optimal solution. Then, low-complexity suboptimal solutions are proposed for each problem by exploiting the characteristics of the optimal solutions. Finally, simulation studies on the effect of user scheduling show that different scheduling strategies should be adopted for STM and TTM.