Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/851
Title: Multi-Objective Optimization of Massive MIMO 5G Wireless Networks towards Power Consumption, Uplink and Downlink Exposure
Authors: Matalatala, Michel
Deruyck, Margot
Shikhantsov, Sergei
Tanghe, Emmeric
Plets, David
Goudos, Sotirios
Psannis, Kostas E.
Martens, Luc
Joseph, Wout
Type: Article
Subjects: FRASCATI::Engineering and technology
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: 5G Networks, Machine Learning
Optimization, Blockchain Technologies
Issue Date: 2019
Source: Applied Sciences
Volume: 9
Issue: 22
First Page: 4974
Abstract: The rapid development of the number of wireless broadband devices requires that the induced uplink exposure be addressed during the design of the future wireless networks, in addition to the downlink exposure due to the transmission of the base stations. In this paper, the positions and power levels of massive MIMO-LTE (Multiple Input Multiple Output-Long Term Evolution) base stations are optimized towards low power consumption, low downlink and uplink electromagnetic exposure and maximal user coverage. A suburban area in Ghent, Belgium has been considered. The results show that the higher the number of BS antenna elements, the fewer number of BSs the massive MIMO network requires. This leads to a decrease of the downlink exposure (−12% for the electric field and −32% for the downlink dose) and an increase of the uplink exposure (+70% for the uplink dose), whereas both downlink and uplink exposure increase with the number of simultaneous served users (+174% for the electric field and +22% for the uplink SAR). The optimal massive MIMO network presenting the better trade-off between the power consumption, the total dose and the user coverage has been obtained with 37 64-antenna BSs. Moreover, the level of the downlink electromagnetic exposure (electric field) of the massive MIMO network is 5 times lower than the 4G reference scenario
URI: https://doi.org/10.3390/app9224974
https://ruomo.lib.uom.gr/handle/7000/851
ISSN: 2076-3417
Other Identifiers: 10.3390/app9224974
Appears in Collections:Department of Applied Informatics

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