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Predicting required licensed spectrum for the future considering big data growth

  • Shayea, Ibraheem (Faculty of Electrical Engineering, Wireless Communication Center, Universiti Teknologi Malaysia) ;
  • Rahman, Tharek Abd. (Faculty of Electrical Engineering, Wireless Communication Center, Universiti Teknologi Malaysia) ;
  • Azmi, Marwan Hadri (Faculty of Electrical Engineering, Wireless Communication Center, Universiti Teknologi Malaysia) ;
  • Han, Chua Tien (Faculty of Electrical Engineering, Wireless Communication Center, Universiti Teknologi Malaysia) ;
  • Arsad, Arsany (Faculty of Electrical Engineering, Wireless Communication Center, Universiti Teknologi Malaysia)
  • Received : 2017.11.12
  • Accepted : 2018.08.01
  • Published : 2019.04.07

Abstract

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.

References

  1. Y. Miao et al., Comprehensive analysis of network traffic data, IEEE Int. Conf. Comput. Inform. Technol., Nadi, Fiji, Dec. 8-10, 2016, pp. 423-430.
  2. R. A. Novo, C. J. Davolos, and Z. John Zhao, Measuring the impact of redirecting and offloading mobile data traffic, Bell Labs Tech. J. 18 (2013), no. 1, 81-103. https://doi.org/10.1002/bltj.21594
  3. J. Zhou et al., Towards traffic minimization for data placement in online social networks, Concurr. Comput. Pract. Exp. 29 (2017), no. 6, e3869. https://doi.org/10.1002/cpe.3869
  4. Y.-K. Song, H. Zo, and A. P. Ciganek, Multi‐criteria evaluation of mobile network sharing policies in Korea, ETRI J. 36 (2014), no. 4, 572-580. https://doi.org/10.4218/etrij.14.0113.1249
  5. H. Jung et al., IDNet: beyond all‐IP network, ETRI J. 37 (2015), no. 5, 833-844. https://doi.org/10.4218/etrij.15.2415.0045
  6. K. Rose, S. Eldridge, and L. Chapin, The internet of things (IoT): An Overview-understanding the issues and challenges of a more connected world, Internet Society, Oct. 2015.
  7. D. Evans, The internet of things: How the next evolution of the internet is changing everything, White paper, Apr. 2011.
  8. G. Association, Understanding the internet of things (IoT), Connected Living Series, New Fetter Lane, London, UK, 2014.
  9. M. Hatton, The global M2M market in 2013, Machina Research White Paper, Jan. 2013.
  10. S. Chen et al., Machine‐to‐machine communications in ultradense networks-A survey, IEEE Commun. Surveys Tut. 19 (2017), no. 3, 1478-1503. https://doi.org/10.1109/COMST.2017.2678518
  11. J.-H. Choi et al., DART: fast and efficient distributed stream processing framework for internet of things, ETRI J. 39 (2017), no. 2, 202-212. https://doi.org/10.4218/etrij.17.2816.0109
  12. S. Lee et al., Forecasting mobile broadband traffic: application of scenario analysis and delphi method, Expert Syst. Appl. 44 (2016), 126-137. https://doi.org/10.1016/j.eswa.2015.09.030
  13. J. Winkler, C. P. J.-W. Kuklinski, and R. Moser, Decision making in emerging markets: the delphi approach's contribution to coping with uncertainty and equivocality, J. Bus. Res. 68 (2015), no. 5, 1118-1126. https://doi.org/10.1016/j.jbusres.2014.11.001
  14. K. Kunisawa and Y. Horibe, Forecasting international telecommunications traffic by the data translation method, Int. J. Forecasting 2 (1986), no. 4, 427-434. https://doi.org/10.1016/0169-2070(86)90089-0
  15. C. Katris and S. Daskalaki, Combining time series forecasting methods for internet traffic, in A. Steland, E. Rafajlowicz, K. Szajowski (Eds.), Stochastic models, statistics and their applications, Springer, New York, NY, 2015, pp. 309-317.
  16. R. Fildes and V. Kumar, Telecommunications demand forecasting-a review, Int. J. Forecasting 18 (2002), no. 4, 489-522. https://doi.org/10.1016/S0169-2070(02)00064-X
  17. A. M. Report, Wireless network data traffic in emerging Asia-Pacific: trends and forecasts 2015-2020, available at www.analysysmason.com.
  18. G. Intelligence, MALAYSIA Markets Data, 3G and 4G mobile technologies and United Nations (World Population Prospects), 2016, available at www.gsma.com
  19. I. Z. Kovacs et al., Mobile broadband traffic forecast modeling for network evolution studies, IEEE Veh. Technol. Conf., San-Francisco, CA, USA, Sept. 5-8, 2011, pp. 1-5.
  20. W.-G. Chung et al., Calculation of spectral efficiency for estimating spectrum requirements of IMT‐advanced in Korean mobile communication environments, ETRI J. 29 (2007), no. 2, 153-161. https://doi.org/10.4218/etrij.07.0106.0105
  21. ITU, Methodology for calculation of spectrum requirements for the terrestrial component of international mobile telecommunications, ITU, Rec.ITU-R M.1768-1, 2013.
  22. ITU, Future spectrum requirements estimate for terrestrial IMT, ITU, Rec. ITU-R M.2290-0, 2013.
  23. ITU, Traffic forecasts and estimated spectrum requirements for the satellite component of IMT 2000 and systems beyond IMT-2000 for the period 2010 to 2020, ITU, Rec. ITU-R M.2077-0, 2006.
  24. F. C. Commission, Mobile broadband: The benefits of additional spectrum, 2010, available at https://www.fcc.gov
  25. B. Williamson, Do you need a Mobile Data Forecast to Estimate Spectrum Demand, 2014, available at https://www.plumvoice.com/
  26. A. A. C. a. M. Authority, Beyond 2020-A spectrum management strategy to address the growth in mobile broadband capacity, 2015.
  27. P. Research, Consulting report prepared for Huawei global wireless spectrum research & analysis, Pyramid Research and Huawei, 2016.
  28. K. Singh, Spectrum usage in mobile cellular, Jan. 2014, available at https://www.Ctalnewsasia.com
  29. M. E. Bayrakdar and A. Calhan, Non‐preemptive queueing model of spectrum handoff scheme based on prioritized data traffic in cognitive wireless networks, ETRI J. 39 (2017), no. 4, 558-569. https://doi.org/10.4218/etrij.17.0116.0850
  30. WCC, Perspectives on Malaysia mobile broadband development (2015-2020), 2017, available at http://wcc.utm.my/
  31. MCMC, MCMC annual reports, Online Access, 2016, available at http://www.mcmc.gov.my
  32. H. mLab, Spectrum gap calculation method, 2016.