Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments

  • Abid Munir Electronic Engineering Department, The Islamia University of Bahawalpur, Pakistan
  • Amjad Ali Department of Electrical Engineering, Jalozai Campus, University of Engineering and Technology Peshawar, Pakistan
  • Abdul Latif Department of Telecommunication, Mehran University of Engineering and Technology, Jamshoro Pakistan

Abstract

Telecom service providers are on a relentless task of development and upgradation of their networks to support increasing requirements of internet users. FTTX landline connections serves multiple users connected on a single line hence need more robust and high data rate capabilities. In FTTX network, link capacity allocation for a service node to aggregating node requires statistical study of usage patterns of customers of the service areas. Different service areas have different usage patterns hence their statistical distributions are different. In this article we have acquired a yearlong data of peak hour utilizations from customers of the largest FTTX service provider in Pakistan to develop a statistical model. We developed an empirical distribution of peak hours of the customers mapped on day clock. This distribution has been used in Monte Carlo computation to find maximum data rate requirements on aggregation nodes in comparison to subscribed data rates of all users on a service node. Further a forecasting model has been used to predict the growth in subscriber demands in different demographic segments for coming years. A combination of maximum possible data rate requirement at aggregation node and forecasted subscriber data rates led to develop a forecast of data rate requirement for next five years in different demographic segments.

Published
Jul 1, 2022
How to Cite
MUNIR, Abid; ALI, Amjad; LATIF, Abdul. Statistical model and forecasting of bandwidth requirements on aggregating nodes of FTTX network using Monte Carlo computations for different demographic segments. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 41, n. 3, p. 85-93, july 2022. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2511>. Date accessed: 16 aug. 2022. doi: http://dx.doi.org/10.22581/muet1982.2203.08.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License