Research Article

Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques

by  Bamidele Moses Kuboye, Tosin Opeyemi Aratunde, Gbadamosi Ayomide A.
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Issue 44
Published: Dec 2021
Authors: Bamidele Moses Kuboye, Tosin Opeyemi Aratunde, Gbadamosi Ayomide A.
10.5120/ijca2021921842
PDF

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

                        @article{ 10.5120/ijca2021921842,
                        author  = { Bamidele Moses Kuboye,Tosin Opeyemi Aratunde,Gbadamosi Ayomide A. },
                        title   = { Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques },
                        journal = { International Journal of Computer Applications },
                        year    = { 2021 },
                        volume  = { 183 },
                        number  = { 44 },
                        pages   = { 9-13 },
                        doi     = { 10.5120/ijca2021921842 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2021
                        %A Bamidele Moses Kuboye
                        %A Tosin Opeyemi Aratunde
                        %A Gbadamosi Ayomide A.
                        %T Users’ Evaluation of Traffic Congestion in LTE Networks using Deep Learning Techniques%T 
                        %J International Journal of Computer Applications
                        %V 183
                        %N 44
                        %P 9-13
                        %R 10.5120/ijca2021921842
                        %I Foundation of Computer Science (FCS), NY, USA
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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Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Long-Term Evolution (LTE) Convolutional Neural Networks (CNN) Long Short-Term Memories (LSTM) Algorithms Traffic subscribers

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