15 Methodology for Modeling the Energy …
237
15.7 Summary
The idea of the multi-level model presented in this paper has the potential to act as
a tool for planning and optimizing current and future telecommunication networks.
The energy and material footprint as the result of the scenario calculations is a valuable source of information on its own, especially when put in relation to national
energy and resource consumption. However, it is also a starting point for further lifecycle impact assessment with an extended range of impact categories (e.g. climate
change, resource depletion, water footprint, etc.). It could also serve as a foundation for deriving key performance indicators and environmental reports, as well as
providing a scientific basis for business solutions, public relations statements and
policy decisions.
The methodology and development of a suitable tool for modeling the energy and
material footprint of future telecommunication networks are still work in progress as
part of the UTAMO project. The next steps will focus on implementing and verifying
possible solutions to the challenges discussed in this paper by developing a tool to
calculate the inventory of the network model and checking the results against existing
data. Thereby the model will be refined at every iterative step.
References
Ali E et al (2017) Beamforming techniques for massive MIMO systems in 5G: overview,
classification, and trends for future research. FITEE 18(6):753–772
Andrae A (2017) Total consumer power consumption forecast. In: Proceedings nordic digital
business summit. Helsinki, Finland
Andrae A (2019) Prediction studies of electricity use of global computing in 2030. Int J Sci Eng
Invest 8(86)
Andrae ASG, Edler T (2015) On global electricity usage of communication technology: trends to
2030. Challenges 6:117–157
Bjornson E, Hoydis J, Sanguinetti L (2017) Massive MIMO networks: spectral, energy, and hardware
efficiency. Now Publishers Inc.
CRM List of European Commission (2017) Online https://ec.europa.eu/growth/sectors/raw-materi
als/specific-interest/critical_en
Fiorani M et al (2016) Modeling energy performance of C-RAN with optical transport in 5G network
scenarios. J Opt Commun Netw 8(11)
Ge X et al (2017) Energy efficiency challenges of 5G small cell networks. IEEE Commun Mag.
https://doi.org/10.1109/mcom.2017.1600788
Hintemann R et al (2017) Boom führt zu deutlich steigendem Energiebedarf der Rechenzentren in
Deutschland im Jahr 2017. Borderstep Institut
IEA 4E EDNA (2019) Intelligent energy efficiency—data centres & wide area networks. Final
Report
Imran M et al (2011) Energy efficiency analysis of the reference systems, areas of improvements
and target breakdown. Tech. Rep. ICT-EARTH deliverable. Tech. Rep. 2011
JRC Science for policy report (2017) Critical raw materials and the circular economy—background
report. Online http://publications.jrc.ec.europa.eu/repository/bitstream/JRC108710/jrc108710pdf-21-12-2017_final.pdf
237
15.7 Summary
The idea of the multi-level model presented in this paper has the potential to act as
a tool for planning and optimizing current and future telecommunication networks.
The energy and material footprint as the result of the scenario calculations is a valuable source of information on its own, especially when put in relation to national
energy and resource consumption. However, it is also a starting point for further lifecycle impact assessment with an extended range of impact categories (e.g. climate
change, resource depletion, water footprint, etc.). It could also serve as a foundation for deriving key performance indicators and environmental reports, as well as
providing a scientific basis for business solutions, public relations statements and
policy decisions.
The methodology and development of a suitable tool for modeling the energy and
material footprint of future telecommunication networks are still work in progress as
part of the UTAMO project. The next steps will focus on implementing and verifying
possible solutions to the challenges discussed in this paper by developing a tool to
calculate the inventory of the network model and checking the results against existing
data. Thereby the model will be refined at every iterative step.
References
Ali E et al (2017) Beamforming techniques for massive MIMO systems in 5G: overview,
classification, and trends for future research. FITEE 18(6):753–772
Andrae A (2017) Total consumer power consumption forecast. In: Proceedings nordic digital
business summit. Helsinki, Finland
Andrae A (2019) Prediction studies of electricity use of global computing in 2030. Int J Sci Eng
Invest 8(86)
Andrae ASG, Edler T (2015) On global electricity usage of communication technology: trends to
2030. Challenges 6:117–157
Bjornson E, Hoydis J, Sanguinetti L (2017) Massive MIMO networks: spectral, energy, and hardware
efficiency. Now Publishers Inc.
CRM List of European Commission (2017) Online https://ec.europa.eu/growth/sectors/raw-materi
als/specific-interest/critical_en
Fiorani M et al (2016) Modeling energy performance of C-RAN with optical transport in 5G network
scenarios. J Opt Commun Netw 8(11)
Ge X et al (2017) Energy efficiency challenges of 5G small cell networks. IEEE Commun Mag.
https://doi.org/10.1109/mcom.2017.1600788
Hintemann R et al (2017) Boom führt zu deutlich steigendem Energiebedarf der Rechenzentren in
Deutschland im Jahr 2017. Borderstep Institut
IEA 4E EDNA (2019) Intelligent energy efficiency—data centres & wide area networks. Final
Report
Imran M et al (2011) Energy efficiency analysis of the reference systems, areas of improvements
and target breakdown. Tech. Rep. ICT-EARTH deliverable. Tech. Rep. 2011
JRC Science for policy report (2017) Critical raw materials and the circular economy—background
report. Online http://publications.jrc.ec.europa.eu/repository/bitstream/JRC108710/jrc108710pdf-21-12-2017_final.pdf