Blog
About

0
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      CELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS

      1 , 1 ,

      Journal of Information and Communication Technology

      UUM Press

      Read this article at

      ScienceOpenPublisher
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.  

          Related collections

          Author and article information

          Contributors
          China
          China
          Vietnam
          Journal
          Journal of Information and Communication Technology
          UUM Press
          December 11 2018
          : 18
          : 1-18
          Affiliations
          [1 ]Key Lab of Information Coding and Transmission, Southwest Jiaotong University, China
          Article
          8277
          10.32890/jict2019.18.1.8277

          All content is freely available without charge to users or their institutions. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission of the publisher or the author. Articles published in the journal are distributed under a http://creativecommons.org/licenses/by/4.0/.

          Comments

          Comment on this article