Cellular network traffic prediction using exponential smoothing methods
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 meth...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2019
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/25516/1/JJCT%2018%201%202018%201-18.pdf http://repo.uum.edu.my/25516/ http://jict.uum.edu.my/index.php/currentissues#aa3 |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | 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. |
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