Price-Based Resource Allocation in Wireless Power Transfer-Enabled Massive MIMO Networks

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 15; p. 3298
• Main Authors: Wang, Zhengqiang, Huang, Kunhao, Yang, Xiaona, Wan, Xiaoyu, Fan, Zifu, Xu, Yongjun
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26-07-2019; MDPI
• Subjects: Communication; Communications networks; Computer simulation; Control algorithms; Energy efficiency; Energy harvesting; Game theory; Internet of Things; massive MIMO; Power management; price; Receivers & amplifiers; Resource allocation; Revenue; Sensors; Stackelberg game; Wireless networks; wireless power transfer; China; massive MIMO; wireless power transfer; Stackelberg game; resource allocation; price
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19153298

This paper considers the price-based resource allocation problem for wireless power transfer (WPT)-enabled massive multiple-input multiple-output (MIMO) networks. The power beacon (PB) can transmit energy to the sensor nodes (SNs) by pricing their harvested energy. Then, the SNs transmit their data to the base station (BS) with large scale antennas by the harvesting energy. The interaction between PB and SNs is modeled as a Stackelberg game. The revenue maximization problem of the PB is transformed into the non-convex optimization problem of the transmit power and the harvesting time of the PB by backward induction. Based on the equivalent convex optimization problem, an optimal resource allocation algorithm is proposed to find the optimal price, energy harvesting time, and power allocation for the PB to maximize its revenue. Finally, simulation results show the effectiveness of the proposed algorithm.