An Improved Call Quality for Call Drop Minimization during Handover in Mobile Communication

Simple item page
dc.contributor.authorTemilola Adedamola JOHN-DEWOLE
dc.date.accessioned2024-10-15T10:58:01Z
dc.date.available2024-10-15T10:58:01Z
dc.date.issued2023-12
dc.description.abstractMobile devices have become essential and significant aspects of everyone's life in the modern technology. Call drops are significant problems for telecommunications network providers. Users’ call quality being negatively affected by mobile call drops, have also lowers revenue generation for telecom service providers. From the literature, the call quality for a group of calls could be predicted based on combination of calls’ successful factors. This study aims at developing an improved call quality for call drop minimization during handover in mobile communication. In order to address the call drop, a model of neural network to enhance call performance and effectiveness was created. Top-performing Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) models were selected and their predictions were combined using a weighted average ensemble approach. The Ensemble Models machine learning approach was employed using Python software. The features used were signal strength, call drop rate, data usage, call types, congestion level, call setup success rate and traffic control congestion rate. The study utilized a dataset with a total of 3000 data points across 30 cell towers and with each cell running for 5 minutes. The performance is evaluated using accuracy, precision, recall, f-score measures and auc-roc. Results of the research gave an accuracy of 97.18 %, 96.64 % precision, 96.58 % recall, 96.11 % f-score measures and auc-roc of 98.79 % for call drop quality. This strongly correlate with existing results of 90 % accuracy, 93 % precision, 92 % recall, 90 % f-score measures but no auc- roc. Another research showed overall accuracy of 95 %. It is therefore recommended that telecommunications companies should implement deep learning techniques on cellular network data to reduce and fix call drops so that consumers will have a higher call quality in the future; providing continuous communications. Keywords: call drop, call quality, call setup success rate, LSTM, CNN, ensemble models, deep learning, telecommunication providers. Word Count: 295
dc.identifier.citationKate Turabia
dc.identifier.otherP.hD
dc.identifier.urihttps://repository.lcu.edu.ng/handle/123456789/802
dc.language.isoen
dc.publisherLead City University, Ibadan
dc.subjectcall drop
dc.subjectcall quality
dc.subjectcall setup success rate
dc.subjectLSTM
dc.subjectCNN
dc.subjectensemble models
dc.subjectdeep learning
dc.subjecttelecommunication providers.
dc.titleAn Improved Call Quality for Call Drop Minimization during Handover in Mobile Communication
dc.typeThesis

Files

Original bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
John-Dewole, Temilola Adedamola Full Thesis, New.pdf
Size:
25.71 MB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
John-Dewole, Temilola Adedamola Preliminary Pages, New.pdf
Size:
2.04 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed to upon submission
Description:
Download

Collections

Department of Computer Science;