Resource Allocation in SWIPT Networks Under a Nonlinear Energy Harvesting Model: Power Efficiency, User Fairness, and Channel Nonreciprocity

• Published in: IEEE transactions on vehicular technology 2018-09, Vol.67 (9), p.8466-8480
• Main Authors: Tran, Ha-Vu, Kaddoum, Georges, Truong, Kien T.
• Format: Article
• Language: English
• Subjects: Channel estimation; Closed form solutions; Downlink; Energy harvesting; Exact solutions; Mathematical models; Multiple objective analysis; Optimization; Pilots; Power efficiency; Power splitters; Power transfer; Radio frequency; Reciprocity; Resource allocation; Resource management; simultaneous wireless information and power transfer; Uplink; Wireless communication
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2018.2848963

This paper considers a multiuser simultaneous wireless information and power transfer system with a nonlinear energy harvesting model, in which a multiantenna base station estimates the downlink channel state information (CSI) via uplink pilots. Each single-antenna user is equipped with a power splitter. Three crucial issues on resource management for this system include: 1) power-efficient improvement, 2) user-fairness guarantee, and 3) nonideal channel reciprocity effect mitigation. Potentially, a resource allocation scheme to address jointly such issues can be devised by using the framework of multiobjective optimization. However, the resulting problem might be complex to solve since the three issues hold different characteristics. Therefore, we propose a novel method to design the resource allocation scheme. In particular, the principle of our method relies on structuralizing mathematically the issues into a cross-layer multilevel optimization problem. On this basis, we then devise solving algorithms and closed-form solutions. Moreover, to instantly adapt the CSI changes in practice while reducing computational burdens, we propose a closed-form suboptimal solution to tackle the problem. Finally, we provide numerical results to show the achievable performance gains using the optimal and suboptimal solutions, and then validate the proposed resource allocation scheme.