Adewale, Adeyinka A. and Ben-Obaje, A. and Noma-Osaghae, Etinosa and Okesola, J. O. and Edikan, Ekong and Abdulkareem, Ademola (2022) Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading. Bulletin of Electrical Engineering and Informatics, 11 (2). pp. 917-925. ISSN 2302-9285
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Abstract
Cellular networks are highly prone to congestion especially at peak traffic periods. This is compounded by the fact that the blocking probability increases. In this study, a machine learning based subscriber classification along with an adaptive Wi-Fi offloading scheme is proposed to improve the throughput and lower the blocking probability of the network. The proposed subscriber classification was implemented using a back propagation based artificial neural network. The result of the subscriber classification was used to develop an adaptive Wi-Fi offloading algorithm based on bandwidth utilization and system throughput. The developed neural network models are shown to be effective, with 94.6% in one experiment, in classifying a user into user classes or levels based on previous data usage. The levenberg–marquardt (LM) algorithm gave the highest accuracy in categorizing the four classes. A relatively large sample size was used for the neural network training cycle and the resulting neural network was then made to use many neurons in its hidden layer. The implementation of the proposed subscriber classification and adaptive Wi-Fi offloading scheme led to a 20% drop in blocking probability and a 50.53% increase in the system throughput.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bandwidth utilization Data traffic Subscriber classification Throughput Wi-Fi offloading |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Patricia Nwokealisi |
| Date Deposited: | 26 Nov 2024 13:18 |
| Last Modified: | 26 Nov 2024 13:18 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/18612 |
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