Joint Distributed Beamforming and Backscattering for UAV-Assisted WPSNs

• Published in: IEEE transactions on wireless communications Vol. 22; no. 3; pp. 1510 - 1522
• Main Authors: Mao, Zhi, Hu, Fengye, Wu, Wen, Wu, Huaqing, Shen, Xuemin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous aerial vehicles; Backscatter; Backscattering; Beamforming; distributed backscattering; distributed beamforming; Energy harvesting; Iterative methods; Kuhn-Tucker method; Nodes; Optimization; Satellite broadcasting; Sensors; Signal strength; Signal to noise ratio; Throughput; unmanned aerial vehicle; Unmanned aerial vehicles; Wireless communication; Wireless powered sensor networks; Wireless sensor networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3204915

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered sensor network (WPSN), where sensor nodes of multiple types can simultaneously harvest radio-frequency energy from the UAV and then transmit sensing data by using harvested energy. A joint distributed beamforming (DBF) and backscattering scheme is designed, in which the sensor nodes of one type can perform DBF while the sensor nodes of other types perform distributed backscattering (DBS) to improve the received signal strength. A sum-throughput maximization problem is formulated by jointly optimizing DBF phases, DBS phases, and time allocation (TA), subject to the received signal-to-noise ratio constraints. Since the formulated problem is difficult to be solved due to the tightly coupled optimizing variables, the problem is decoupled into a TA subproblem and a phase optimization subproblem, and then a two-step algorithm is proposed to solve them. Firstly, the closed-form solution for the TA subproblem is derived according to Karush-Kuhn-Tucker conditions. Secondly, based on iterative optimization and one-dimensional search methods, a centralized algorithm is proposed to obtain the optimal solution for the phase optimization subproblem. Moreover, a decentralized algorithm that obtains the suboptimal solution is proposed to reduce the computational complexity. Extensive simulation results validate the effectiveness of the proposed scheme on throughput enhancement.