Spectrum Demand Forecasting for IoT Services
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 traf...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/13/9/232 |
| _version_ | 1797519211528978432 |
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| author | Daniel Jaramillo-Ramirez Manuel Perez |
| author_facet | Daniel Jaramillo-Ramirez Manuel Perez |
| author_sort | Daniel Jaramillo-Ramirez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T07:39:46Z |
| format | Article |
| id | doaj.art-886b65c2b9174a4fbea6b413b7b12170 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-10T07:39:46Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-886b65c2b9174a4fbea6b413b7b121702023-11-22T13:10:21ZengMDPI AGFuture Internet1999-59032021-09-0113923210.3390/fi13090232Spectrum Demand Forecasting for IoT ServicesDaniel Jaramillo-Ramirez0Manuel Perez1Electronics Department, School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, ColombiaElectronics Department, School of Engineering, Pontificia Universidad Javeriana, Bogota 110231, ColombiaThe 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.https://www.mdpi.com/1999-5903/13/9/232Internet of Things (IoT)Machine to Machine (M2M)radio spectrumspectrum requirementsspectrum calculationtraffic prediction |
| spellingShingle | Daniel Jaramillo-Ramirez Manuel Perez Spectrum Demand Forecasting for IoT Services Future Internet Internet of Things (IoT) Machine to Machine (M2M) radio spectrum spectrum requirements spectrum calculation traffic prediction |
| title | Spectrum Demand Forecasting for IoT Services |
| title_full | Spectrum Demand Forecasting for IoT Services |
| title_fullStr | Spectrum Demand Forecasting for IoT Services |
| title_full_unstemmed | Spectrum Demand Forecasting for IoT Services |
| title_short | Spectrum Demand Forecasting for IoT Services |
| title_sort | spectrum demand forecasting for iot services |
| topic | Internet of Things (IoT) Machine to Machine (M2M) radio spectrum spectrum requirements spectrum calculation traffic prediction |
| url | https://www.mdpi.com/1999-5903/13/9/232 |
| work_keys_str_mv | AT danieljaramilloramirez spectrumdemandforecastingforiotservices AT manuelperez spectrumdemandforecastingforiotservices |