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. 2021 Dec 30;22(1):255.
doi: 10.3390/s22010255.

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

Josip Lorincz  1 , Zonimir Klarin  2
Affiliations

How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks

Josip Lorincz et al. Sensors (Basel). .

Abstract

As the rapid growth of mobile users and Internet-of-Everything devices will continue in the upcoming decade, more and more network capacity will be needed to accommodate such a constant increase in data volumes (DVs). To satisfy such a vast DV increase, the implementation of the fifth-generation (5G) and future sixth-generation (6G) mobile networks will be based on heterogeneous networks (HetNets) composed of macro base stations (BSs) dedicated to ensuring basic signal coverage and capacity, and small BSs dedicated to satisfying capacity for increased DVs at locations of traffic hotspots. An approach that can accommodate constantly increasing DVs is based on adding additional capacity in the network through the deployment of new BSs as DV increases. Such an approach represents an implementation challenge to mobile network operators (MNOs), which is reflected in the increased power consumption of the radio access part of the mobile network and degradation of network energy efficiency (EE). In this study, the impact of the expected increase of DVs through the 2020s on the EE of the 5G radio access network (RAN) was analyzed by using standardized data and coverage EE metrics. An analysis was performed for five different macro and small 5G BS implementation and operation scenarios and for rural, urban, dense-urban and indoor-hotspot device density classes (areas). The results of analyses reveal a strong influence of increasing DV trends on standardized data and coverage EE metrics of 5G HetNets. For every device density class characterized with increased DVs, we here elaborate on the process of achieving the best and worse combination of data and coverage EE metrics for each of the analyzed 5G BSs deployment and operation approaches. This elaboration is further extended on the analyses of the impact of 5G RAN instant power consumption and 5G RAN yearly energy consumption on values of standardized EE metrics. The presented analyses can serve as a reference in the selection of the most appropriate 5G BS deployment and operation approach, which will simultaneously ensure the transfer of permanently increasing DVs in a specific device density class and the highest possible levels of data and coverage EE metrics.

Keywords: 5G; base station; coverage; data; energy-efficiency; green communications; green networking; metric; mobile network operator; power; radio access network; wireless.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Visualization of analyzed 5G HetNet deployments for different device density classes with the maximal number of installed small and macro BSs per square kilometer area.
Figure 2
Figure 2
Impact of DV increase on data and coverage EE metrics for each year during the 2020s for (a) indoor-hotspot, (b) dense-urban, (c) urban and (d) rural device density class.
Figure 3
Figure 3
Impact of DV increase on data EE metrics and instant 5G network power consumption during the 2020s for (a) indoor-hotspot, (b) dense-urban, (c) urban and (d) rural device density class.
Figure 4
Figure 4
Impact of DV increase on coverage EE metrics and 5G network yearly energy consumption during the 2020s for (a) indoor-hotspot, (b) dense-urban, (c) urban and (d) rural device density class.
See this image and copyright information in PMC

References

    1. Jejdling F. Ericsson Mobility Report. Ericsson; Stockholm, Sweden: 2021.
    1. Transforma Insights Number of Internet of Things (IoT) Connected Devices Worldwide from 2019 to 2030, by Communications Technology (in Millions) 2020. [(accessed on 10 August 2021)]. Available online: https://www.statista.com/statistics/1194688/iot-connected-devices-commun...
    1. ITU-R. IMT Vision—Framework and Overall Objectives of the Future Development of IMT for 2020 and Beyond. 2015. [(accessed on 4 August 2021)]. Available online: https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2083-0-201509-I!!PDF-....
    1. Lorincz J., Matijevic T., Petrovic G. On interdependence among transmit and consumed power of macro base station technologies. Comput. Commun. 2014;50:10–28. doi: 10.1016/j.comcom.2014.02.010. - DOI
    1. Lorincz J., Bogarelli M., Capone A., Begušić D. Heuristic approach for optimized energy savings in wireless access networks; Proceedings of the SoftCOM 2010, 18th International Conference on Software, Telecommunications and Computer Networks; Split, Croatia. 23–25 September 2010; Manhattan, NY, USA: IEEE; 2010. pp. 60–65.

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