Forecasting international telecommunications traffic by the data translation method

Kunisawa, Kiyonori; Horibe, Yasuichi

doi:10.1016/0169-2070(86)90089-0

Cited by 6 publications

(4 citation statements)

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“…Although they used the same shape for different areas, different saturation values were applied, to reflect different income levels. A quantitative mobile broadband traffic forecast model based on the Gomperz function was suggested, providing sufficient flexibility for predicting various traffic growth scenarios (Kovacs et al, 2011, Kunisawa, 1986. This also exemplifies the use of the proposed traffic-forecasting model, with a network evolution case study, in the generic setting of a dense urban European network deployment scenario.…”

Section: Accepted Manuscriptmentioning

confidence: 95%

Forecasting mobile broadband traffic: Application of scenario analysis and Delphi method

Lee

Cho

Hong

et al. 2016

Expert Systems with Applications

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Section: Accepted Manuscriptmentioning

confidence: 95%

Forecasting mobile broadband traffic: Application of scenario analysis and Delphi method

Lee

Cho

Hong

et al. 2016

Expert Systems with Applications

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“…The spectrum gap has an inverse relationship with the spectrum efficiency [20]. To estimate the SEG, η fy , can be calculated as a ratio between the spectrum efficiencies of the corresponding year to the previous year; as simplified in (14).…”

Section: Spectrum Efficiency Growthmentioning

confidence: 99%

“…To this end, several forecasting models have been developed to predict mobile data traffic (MDT), and used to forecast the required licensed MBB spectrum. Some of the developed models used to forecast mobile data demands include the Delphi model , data translation model , combining time series models for forecasting , Sungjoo Lee model , diffusion modeling , Analysys Mason model , GSMA model , and the model by István Z. Kovács et al . According to the licensed MBB spectrum, which is the main focus of this study, several models have been developed to forecast the future‐required spectrum ; for example, the International Telecommunication Union (ITU) model , Federal Communications Commission (FCC) model , Plum Insight model , ACMA‐engaged Analysys Mason model , and Pyramid Research model .…”

Section: Introductionmentioning

confidence: 99%

Predicting required licensed spectrum for the future considering big data growth

et al. 2019

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This paper proposes a new spectrum forecasting (SF) model to estimate the spectrum demands for future mobile broadband (MBB) services. The model requires five main input metrics, that is, the current available spectrum, site number growth, mobile data traffic growth, average network utilization, and spectrum efficiency growth. Using the proposed SF model, the future MBB spectrum demand for Malaysia in 2020 is forecasted based on the input market data of four major mobile telecommunication operators represented by A–D, which account for approximately 95% of the local mobile market share. Statistical data to generate the five input metrics were obtained from prominent agencies, such as the Malaysian Communications and Multimedia Commission, OpenSignal, Analysys Mason, GSMA, and Huawei. Our forecasting results indicate that by 2020, Malaysia would require approximately 307 MHz of additional spectrum to fulfill the enormous increase in mobile broadband data demands.

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“…For that, several forecasting models were developed to predict mobile data traffic, and were then used to forecast the required licensed MBB spectrum. Some of the developed models used to forecast mobile data demands include the Delphi model [46], data translation model [47], combining time series models for forecasting [48], the Sungjoo Lee model [49], diffusion modelling [50] and trend extrapolation [50]. Analysys Mason [51] is one of the most expert in global analysis and consultative services in the telecommunications and technology market.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Gap Analysis With Practical Solutions for Future Mobile Data Traffic Growth in Malaysia

et al. 2019

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In this paper, an efficient spectrum forecasting model is developed to estimate the required spectrum and calculate the spectrum gap in future. This developed model is essentially based on five main metrics and one constant. The five main metrics are the currently available spectrum, sites number growth, data traffic growth, average network utilization, and spectrum efficiency growth. The constant metric is considered to give a space for our model to be used in another country or when a new technology is coming. This developed model is then used to forecast the required spectrum and spectrum gap for Malaysia in 2020. The estimation is performed based on the input market data of four main mobile telecommunication operators in Malaysia: Maxis, Celcom, Digi, and U-Mobile. The input data for this model are collected from various sources, such as the Malaysian Communications and Multimedia Commission, OpenSignal, Analysys Mason, GSMA, and HUAWEI. The results indicate that by 2020, Malaysia will require around 307 MHz of additional spectrum to fulfill the enormous increase of mobile data demands. Addressing this increment can be achieved by launching additional spectrum bands, enhancing spectrum efficiency, offloading mobile data to unlicensed bands or deploying more site numbers. INDEX TERMS Mathematical forecasting spectrum model, forecasting required spectrum, spectrum gap, Malaysia's spectrum in future.

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Forecasting international telecommunications traffic by the data translation method

Cited by 6 publications

References 0 publications

Forecasting mobile broadband traffic: Application of scenario analysis and Delphi method

Forecasting mobile broadband traffic: Application of scenario analysis and Delphi method

Predicting required licensed spectrum for the future considering big data growth

Spectrum Gap Analysis With Practical Solutions for Future Mobile Data Traffic Growth in Malaysia

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