Energy efficient optimisation for large-scale multiple-antenna system with WPT

Abstract
In this study, an energy-efficient optimisation scheme for a large-scale multiple-antenna system with wireless power transfer (WPT) is presented. In the considered system, the user is charged by a base station with a large number of antennas via downlink WPT and then utilises the received power to carry out uplink data transmission. Novel antenna selection, time allocation and power allocation schemes are presented to optimise the energy efficiency of the overall system. In addition, the authors also consider channel state information cannot be perfectly obtained when designing the resource allocation schemes. The non-linear fractional programming-based algorithm is utilised to address the formulated problem. Their proposed schemes are validated by extensive simulations and it shows superior performance over the existing schemes.
Main Authors
Format
Articles Research article
Published
2018
Series
Subjects
Publication in research information system
Publisher
IET
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-201811194769Use this for linking
Review status
Peer reviewed
ISSN
1751-8628
DOI
https://doi.org/10.1049/iet-com.2017.0472
Language
English
Published in
IET Communications
Citation
  • Chang, Z., Zhang, S., Wang, Z., Guo, X., Han, Z., & Ristaniemi, T. (2018). Energy efficient optimisation for large-scale multiple-antenna system with WPT. IET Communications, 12(5), 552-558. https://doi.org/10.1049/iet-com.2017.0472
License
In CopyrightOpen Access
Funder(s)
Research Council of Finland
Funding program(s)
KV-yhteishanke, SA
Joint International Project, AoF
Research Council of Finland
Additional information about funding
This work is partly supported by the Academy of Finland (Decision number 284748), Hebei NSF (E2017203351), U.S. National Natural Science Foundation under Grant CNS-1702850, Grant CNS-1646607, Grant ECCS-1547201, Grant CCF-1456921, Grant CMMI-1434789, Grant CNS-1443917, and Grant ECCS-1405121.
Copyright© IET Communications, 2018.

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