IDEAS home Printed from https://ideas.repec.org/p/zbw/itse19/205169.html
   My bibliography  Save this paper

Feasibility of the City-driven Neutral Host Operator: The case of Helsinki

Author

Listed:
  • Benseny, Jaume
  • Walia, Jaspreet
  • Finley, Benjamin
  • Hämmäinen, Heikki

Abstract

The large-scale deployment of 5G small cells in an urban environment can be facilitated by allowing the placement of antennas on light poles and by organizing their commercial exploitation via a neutral operator. In this paper, we analyze the feasibility of a city-driven Neutral Host Operator (NHO) for the case of the Helsinki Metropolitan Area by comparing the costs of two alternative small cell deployment strategies. We estimate 5G deployment needs at the postal code level for three future demand scenarios considering different data consumption volumes as well as number of subscriptions for connected devices. For this, we add postal code capacity in a year basis updating existing macrocell sites to 5G and deploying new 5G macro/small cell sites depending on indoor/outdoor demand and offloading. By 2030, about 85%, 90%, and 92% of existing 4G macro sites need to be updated with 5G. Additionally, 34, 816, and 1035 outdoor small cells will be required to serve the three demand growth scenarios, respectively. Although the NHO-driven deployment strategy initially incurs higher costs than the MNO-driven, before 2030 the former accumulateslower costs for all demand scenarios. In case the NHO-driven strategy achieves a 20% cost-savings in public works, this strategy becomes costadvantageous in 2029 with 728 and 921 deployed small cells for the medium and high growth scenarios, respectively. Cost-saving mechanisms in the NHO-driven strategy should focus on public works since they are the largest contributor to the deployment cost.

Suggested Citation

  • Benseny, Jaume & Walia, Jaspreet & Finley, Benjamin & Hämmäinen, Heikki, 2019. "Feasibility of the City-driven Neutral Host Operator: The case of Helsinki," 30th European Regional ITS Conference, Helsinki 2019 205169, International Telecommunications Society (ITS).
  • Handle: RePEc:zbw:itse19:205169
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/205169/1/Benseny-et-al.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Oughton, Edward J. & Frias, Zoraida, 2018. "The cost, coverage and rollout implications of 5G infrastructure in Britain," Telecommunications Policy, Elsevier, vol. 42(8), pages 636-652.
    3. Boeing, Geoff, 2018. "Urban Spatial Order: Street Network Orientation, Configuration, and Entropy," SocArXiv qj3p5, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lähteenmäki, Jarno, 2021. "The evolution paths of neutral host businesses: Antecedents, strategies, and business models," Telecommunications Policy, Elsevier, vol. 45(10).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jorge-Eusebio Velasco-López & Ramón-Alberto Carrasco & Jesús Serrano-Guerrero & Francisco Chiclana, 2024. "Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    2. Fadaki, Masih & Asadikia, Atie, 2024. "Augmenting Monte Carlo Tree Search for managing service level agreements," International Journal of Production Economics, Elsevier, vol. 271(C).
    3. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    4. Maghsoodi, Abtin Ijadi, 2023. "Cryptocurrency portfolio allocation using a novel hybrid and predictive big data decision support system," Omega, Elsevier, vol. 115(C).
    5. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    6. Fadi Kahwash & Basel Barakat & Ahmad Taha & Qammer H. Abbasi & Muhammad Ali Imran, 2021. "Optimising Electrical Power Supply Sustainability Using a Grid-Connected Hybrid Renewable Energy System—An NHS Hospital Case Study," Energies, MDPI, vol. 14(21), pages 1-23, October.
    7. Shiqin Liu & Carl Higgs & Jonathan Arundel & Geoff Boeing & Nicholas Cerdera & David Moctezuma & Ester Cerin & Deepti Adlakha & Melanie Lowe & Billie Giles-Corti, 2021. "A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data," Papers 2105.08814, arXiv.org.
    8. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    9. Romero-Fiances, Irene & Livera, Andreas & Theristis, Marios & Makrides, George & Stein, Joshua S. & Nofuentes, Gustavo & de la Casa, Juan & Georghiou, George E., 2022. "Impact of duration and missing data on the long-term photovoltaic degradation rate estimation," Renewable Energy, Elsevier, vol. 181(C), pages 738-748.
    10. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    11. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    12. Ashish Shrestha & Bishal Ghimire & Francisco Gonzalez-Longatt, 2021. "A Bayesian Model to Forecast the Time Series Kinetic Energy Data for a Power System," Energies, MDPI, vol. 14(11), pages 1-15, June.
    13. Nik Dawson & Sacha Molitorisz & Marian-Andrei Rizoiu & Peter Fray, 2020. "Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020," Papers 2008.12459, arXiv.org, revised Feb 2021.
    14. Luyao Zhang & Fan Zhang, 2023. "Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective," Papers 2305.02552, arXiv.org.
    15. Md Jamal Ahmed Shohan & Md Omar Faruque & Simon Y. Foo, 2022. "Forecasting of Electric Load Using a Hybrid LSTM-Neural Prophet Model," Energies, MDPI, vol. 15(6), pages 1-18, March.
    16. Bauer, Johannes M. & Bohlin, Erik, 2022. "Regulation and innovation in 5G markets," Telecommunications Policy, Elsevier, vol. 46(4).
    17. Ángel Cuevas & Ramiro Ledo & Enrique M. Quilis, 2021. "Seasonal adjustment of the Spanish sales daily data," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 687-708, December.
    18. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    19. Md. Iftekharul Alam Efat & Petr Hajek & Mohammad Zoynul Abedin & Rahat Uddin Azad & Md. Al Jaber & Shuvra Aditya & Mohammad Kabir Hassan, 2024. "Deep-learning model using hybrid adaptive trend estimated series for modelling and forecasting sales," Annals of Operations Research, Springer, vol. 339(1), pages 297-328, August.
    20. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:itse19:205169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: http://www.itseurope.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.