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Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 44 |
| Year of Publication: 2021 |
| Authors: Bamidele Moses Kuboye, Tosin Opeyemi Aratunde, Gbadamosi Ayomide A. |
10.5120/ijca2021921842
|
Bamidele Moses Kuboye, Tosin Opeyemi Aratunde, Gbadamosi Ayomide A. . Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques. International Journal of Computer Applications. 183, 44 ( Dec 2021), 9-13. DOI=10.5120/ijca2021921842
Abstract
Deep learning is a division of machine learning built on a set of algorithms that attempt to model high-level abstractions in data by using prototypical architectures with complex structures. This work is based on using Deep learning to predict congestion on Long-Term Evolution (LTE). The work evaluates existence of traffic congestion in LTE networks using Convolutional Neural Networks (CNN) and Long Short-Term Memories (LSTMs) as Deep learning techniques. The accuracy from the results of both algorithms was compared to show the better algorithm on the prediction. The final accuracy of the deep learning model is given at 82% (0.82) which is the result of prediction with LSTM. Thus, LSTM proved to be more accurate in predicting the existence of congestion on the dataset. Prediction done with CNN and LSTM on the data collected showed that majority of LTE networks users suffer traffic congestion often.
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