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All Journal Bulletin of Electrical Engineering and Informatics
Ben Obaje Abraham
Covenant University
Electrical & Electronics Engineering

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Cellular network bandwidth improvement using subscribers’ classification and Wi-Fi offloading Adewale Adeyinka Ajao; Ben Obaje Abraham; Etinosa Noma Osaghae; Okesola Olatunji; Edikan Ekong; Abdulkareem Ademola
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.2575

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.
Co-Authors Abdulkareem Ademola Adewale Adeyinka Ajao Edikan Ekong Etinosa Noma Osaghae Okesola Olatunji