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Spectrum Demand Forecasting for IoT Services

Author

Listed:
  • Daniel Jaramillo-Ramirez

    (Electronics Department, School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, Colombia
    These authors contributed equally to this work.)

  • Manuel Perez

    (Electronics Department, School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, Colombia
    These authors contributed equally to this work.)

Abstract

The evolution of IoT has come with the challenge of connecting not only a massive number of devices, but also providing an always wider variety of services. In the next few years, a big increase in the number of connected devices is expected, together with an important increase in the amount of traffic generated. Never before have wireless communications permeated so deeply in all industries and economic sectors. Therefore, it is crucial to correctly forecast the spectrum needs, which bands should be used for which services, and the economic potential of its utilization. This paper proposes a methodology for spectrum forecasting consisting of two phases: a market study and a spectrum forecasting model. The market study determines the main drivers of the IoT industry for any country: services, technologies, frequency bands, and the number of devices that will require IoT connectivity. The forecasting model takes the market study as the input and calculates the spectrum demand in 5 steps: Defining scenarios for spectrum contention, calculating the offered traffic load, calculating a capacity for some QoS requirements, finding the spectrum required, and adjusting according to key spectral efficiency determinants. This methodology is applied for Colombia’s IoT spectrum forecast. We provide a complete step-by-step implementation in fourteen independent spectrum contention scenarios, calculating offered traffic, required capacity, and spectrum for cellular licensed bands and non-cellular unlicensed bands in a 10-year period. Detailed results are presented specifying coverage area requirements per economic sector, frequency band, and service. The need for higher teledensity and higher spectral efficiency turns out to be a determining factor for spectrum savings.

Suggested Citation

  • Daniel Jaramillo-Ramirez & Manuel Perez, 2021. "Spectrum Demand Forecasting for IoT Services," Future Internet, MDPI, vol. 13(9), pages 1-24, September.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:9:p:232-:d:631077
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    References listed on IDEAS

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