Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137164
Title: Nonlinear energy harvesting for millimeter wave networks with large-scale antennas
Authors: Tran, Tuong Xuan
Wang, Wei
Luo, Sheng
Teh, Kah Chan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Tran, T. X., Wang, W., Luo, S., & Teh, K. C. (2018). Nonlinear energy harvesting for millimeter wave networks with large-scale antennas. IEEE Transactions on Vehicular Technology, 67(10), 9488-9498. doi:10.1109/TVT.2018.2856500
Journal: IEEE Transactions on Vehicular Technology
Abstract: In this paper, an analytical framework is proposed to explore the potential of wireless power transfer (WPT) for a millimeter wave (mmWave) network with large-scale antennas. Prior works mostly focus on WPT systems with a linear energy harvesting (EH) model, which cannot properly capture the power dependent EH efficiency. On the other hand, based on a nonlinear EH model, the effect of practical EH circuit specifications on the EH performance can be analyzed. By using stochastic geometry approach, analytical and asymptotic expressions for the energy coverage probability, average harvested energy, and achievable rate are derived under different base station (BS) configuration and deployment scenarios. Numerical results provide interesting design insights that the EH circuit specifications significantly affect the network performance. Increasing the BS density and number of BS antennas can improve the network performance. Due to saturation of EH circuits, the typical receiver can only achieve finite maximum amount of harvested energy and achievable rate even when the BS has a large number of antennas.
URI: https://hdl.handle.net/10356/137164
ISSN: 0018-9545
DOI: 10.1109/TVT.2018.2856500
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TVT.2018.2856500
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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