Joint Power Allocation and Splitting Control for SWIPT-Enabled NOMA Systems

• Published in: IEEE transactions on wireless communications Vol. 19; no. 1; pp. 120 - 133
• Main Authors: Tang, Jie, Yu, Yu, Liu, Mingqian, So, Daniel K. C., Zhang, Xiuyin, Li, Zan, Wong, Kai-Kit
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Energy harvesting; Energy requirements; Maximization; Multiple objective analysis; NOMA; Non-orthogonal multiple access (NOMA); Nonorthogonal multiple access; Optimization; Power management; power splitting (PS); Power transfer; Resource management; simultaneous wireless information and power transfer (SWIPT); Splitting; Throughput; Wireless communication
• DOI: 10.1109/TWC.2019.2942303
• ISSN: 1536-1276

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.