A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks

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Published: Jul 13, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i7s.7147
Keywords:
Call dropout, chimp optimization algorithm, data preprocessing, linear kernel regression, mobile networks, machine learning, service provider

Main Article Content

Ashok G V
Department of Computer Science and Engineering, Dayananda Sagar University, Bangalore, Karnataka, India
Vasanthi Kumari P.
Professor, Department of Computer Applications, Dayananda Sagar University, Bangalore, Karnataka, India

Abstract

Call failure can be caused by a variety of factors, including inadequate cellular infrastructure, undesirable system structuring, busy mobile phone towers, changing between towers, and many more. Outdated equipment and networks worsen call failure, and installing more towers to improve coverage might harm the regional ecosystems. In the existing studies, a variety of machine learning algorithms are implemented for call drop prediction in the mobile networks. But it facing problems in terms of high error rate, low prediction accuracy, system complexity, and more training time. Therefore, the proposed work intends to develop a new and sophisticated framework, named as, Chimp Optimized Linear Kernel Regression (COLKR) for predicting call drops in the mobile networks. For the analysis, the Call Detail Record (CDR) has been collected and used in this framework. By preprocessing the attributes, the normalized dataset is constructed using the median regression-based filtering technique. To extract the most significant features for training the classifier with minimum processing complexity, a sophisticated Chimp Optimization Algorithm (COA) is applied. Then, a new machine learning model known as the Linear Kernel Regression Model (LKRM) has been deployed to predict call drops with greater accuracy and less error. For the performance assessment of COLKR, several machine learning classifiers are compared with the proposed model using a variety of measures. By using the proposed COLKR mechanism, the call drop detection accuracy is improved to 99.4%, and the error rate is reduced to 0.098%, which determines the efficiency and superiority of the proposed system.

Article Details

How to Cite
G V, A. ., & Kumari P., V. . (2023). A Novel Chimp Optimized Linear Kernel Regression (COLKR) Model for Call Drop Prediction in Mobile Networks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7s), 593–603. https://doi.org/10.17762/ijritcc.v11i7s.7147
Issue
Vol. 11 No. 7s (2023)
Section
Articles

References

M. McClellan, C. Cervelló-Pastor, and S. Sallent, "Deep learning at the mobile edge: Opportunities for 5G networks," Applied Sciences, vol. 10, p. 4735, 2020.

R. Su, D. Zhang, R. Venkatesan, Z. Gong, C. Li, F. Ding, et al., "Resource allocation for network slicing in 5G telecommunication networks: A survey of principles and models," IEEE Network, vol. 33, pp. 172-179, 2019.

M.-F. Huang, M. Salemi, Y. Chen, J. Zhao, T. J. Xia, G. A. Wellbrock, et al., "First field trial of distributed fiber optical sensing and high-speed communication over an operational telecom network," Journal of Lightwave Technology, vol. 38, pp. 75-81, 2019.

I. Tomkos, D. Klonidis, E. Pikasis, and S. Theodoridis, "Toward the 6G network era: Opportunities and challenges," IT Professional, vol. 22, pp. 34-38, 2020.

K. Hirata, H. Yamamoto, S. Kamamura, T. Oka, Y. Uematsu, H. Maeda, et al., "System design for traveling maintenance in wide-area telecommunication networks," IEICE Transactions on Communications, vol. 103, pp. 363-374, 2020.

A. Muradova and K. Khujamatov, "Results of calculations of parameters of reliability of restored devices of the multiservice communication network," in 2019 International Conference on Information Science and Communications Technologies (ICISCT), 2019, pp. 1-4.

H. Abdulkareem, A. Tekanyi, A. Kassim, Z. Muhammad, U. Almustapha, and H. Adamu, "Analysis of a GSM network quality of service using call drop rate and call setup success rate as performance indicators," in Proceedings of: 2nd International Conference of the IEEE Nigeria, 2019, p. 300.

M. Duraipandian, "Long term evolution-self organizing network for minimization of sudden call termination in mobile radio access networks," Journal of trends in Computer Science and Smart technology (TCSST), vol. 2, pp. 89-97, 2020.

O. O. Erunkulu, E. N. Onwuka, O. Ugweje, and L. A. Ajao, "Prediction of call drops in GSM network using artificial neural network," Jurnal Teknologi dan Sistem Komputer, vol. 7, pp. 38-46, 2019.

L. Xu, X. Zhao, Y. Yu, Y. Luan, L. Zhao, X. Cheng, et al., "A comprehensive operation and revenue analysis algorithm for LTE/5G wireless system based on telecom operator data," in 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2019, pp. 1521-1524.

A. Alhammadi, M. Roslee, M. Y. Alias, I. Shayea, S. Alraih, and K. S. Mohamed, "Auto tuning self-optimization algorithm for mobility management in LTE-A and 5G HetNets," IEEE Access, vol. 8, pp. 294-304, 2019.

Z. Mammeri, "Reinforcement learning based routing in networks: Review and classification of approaches," Ieee Access, vol. 7, pp. 55916-55950, 2019.

O. A. Wahab, A. Mourad, H. Otrok, and T. Taleb, "Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems," IEEE Communications Surveys & Tutorials, vol. 23, pp. 1342-1397, 2021.

G. Manogaran, M. Alazab, V. Saravanan, B. S. Rawal, P. M. Shakeel, R. Sundarasekar, et al., "Machine learning assisted information management scheme in service concentrated IoT," IEEE transactions on industrial informatics, vol. 17, pp. 2871-2879, 2020.

I. Ullah, B. Raza, A. K. Malik, M. Imran, S. U. Islam, and S. W. Kim, "A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector," IEEE access, vol. 7, pp. 60134-60149, 2019.

F. Tang, B. Mao, Y. Kawamoto, and N. Kato, "Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption," IEEE Communications Surveys & Tutorials, vol. 23, pp. 1578-1598, 2021.

M. Ozturk, M. Gogate, O. Onireti, A. Adeel, A. Hussain, and M. A. Imran, "A novel deep learning driven, low-cost mobility prediction approach for 5G cellular networks: The case of the Control/Data Separation Architecture (CDSA)," Neurocomputing, vol. 358, pp. 479-489, 2019/09/17/ 2019.

Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, "Application of machine learning in wireless networks: Key techniques and open issues," IEEE Communications Surveys & Tutorials, vol. 21, pp. 3072-3108, 2019.

M. S. H. Abad, E. Ozfatura, D. Gunduz, and O. Ercetin, "Hierarchical federated learning across heterogeneous cellular networks," in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020, pp. 8866-8870.

A. K. Ahmad, A. Jafar, and K. Aljoumaa, "Customer churn prediction in telecom using machine learning in big data platform," Journal of Big Data, vol. 6, pp. 1-24, 2019.

G. Luo, Q. Yuan, J. Li, S. Wang, and F. Yang, "Artificial intelligence powered mobile networks: From cognition to decision," IEEE Network, vol. 36, pp. 136-144, 2022.

J. Moysen and L. Giupponi, "From 4G to 5G: Self-organized network management meets machine learning," Computer Communications, vol. 129, pp. 248-268, 2018.

A. Anand and G. de Veciana, "Resource allocation and HARQ optimization for URLLC traffic in 5G wireless networks," IEEE Journal on Selected Areas in Communications, vol. 36, pp. 2411-2421, 2018.

A. Asghar, H. Farooq, and A. Imran, "Self-healing in emerging cellular networks: Review, challenges, and research directions," IEEE Communications Surveys & Tutorials, vol. 20, pp. 1682-1709, 2018.

M. S. Hadi, A. Q. Lawey, T. E. El-Gorashi, and J. M. Elmirghani, "Big data analytics for wireless and wired network design: A survey," Computer Networks, vol. 132, pp. 180-199, 2018.

I. V. Pustokhina, D. A. Pustokhin, P. T. Nguyen, M. Elhoseny, and K. Shankar, "Multi-objective rain optimization algorithm with WELM model for customer churn prediction in telecommunication sector," Complex & Intelligent Systems, 2021/04/05 2021.

G. RM, "Prediction of customer plan using churn analysis for telecom industry," Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), vol. 13, pp. 926-929, 2020.

D. Mishra and A. Mishra, "Self-optimization in LTE: An approach to reduce call drops in mobile network," in Futuristic Trends in Network and Communication Technologies: First International Conference, FTNCT 2018, Solan, India, February 9–10, 2018, Revised Selected Papers 1, 2019, pp. 382-395.

G. Ashok and P. V. Kumari, "To Analyse and optimise the issues in call drops and enhance the real time (CDR) processing," EasyChair 2516-2314, 2020.

G. Ashok and V. Kumari, "An investigation of various machine learning techniques for mobile call data analysis for reducing call drop," Materials Today: Proceedings, vol. 51, pp. 2476-2478, 2022.

G. Kaur, R. K. Goyal, and R. Mehta, "An efficient handover mechanism for 5G networks using hybridization of LSTM and SVM," Multimedia Tools and Applications, vol. 81, pp. 37057-37085, 2022.

S. Verma, O. Singh, S. Kumar, and S. Mishra, "Fuzzy—KNN-Assisted Vehicular Localization for Bluetooth and Wi-Fi Scenario," in Emerging Technologies for Computing, Communication and Smart Cities: Proceedings of ETCCS 2021, ed: Springer, 2022, pp. 451-465.

C. Gupta, I. Johri, K. Srinivasan, Y.-C. Hu, S. M. Qaisar, and K.-Y. Huang, "A systematic review on machine learning and deep learning models for electronic information security in mobile networks," Sensors, vol. 22, p. 2017, 2022.

K. Mandal and G. Gong, "PrivFL: Practical privacy-preserving federated regressions on high-dimensional data over mobile networks," in Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop, 2019, pp. 57-68.

D. Clemente, G. Soares, D. Fernandes, R. Cortesao, P. Sebastiao, and L. S. Ferreira, "Traffic forecast in mobile networks: Classification system using machine learning," in 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019, pp. 1-5.

E. Alimpertis, A. Markopoulou, C. Butts, and K. Psounis, "City-wide signal strength maps: Prediction with random forests," in The World Wide Web Conference, 2019, pp. 2536-2542.

K. Abbas, T. A. Khan, M. Afaq, and W.-C. Song, "Ensemble learning-based network data analytics for network slice orchestration and management: An intent-based networking mechanism," in NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 2022, pp. 1-5.

X. Liu and T. Wang, "Application of XGBOOST model on potential 5G mobile users forecast," in Signal and Information Processing, Networking and Computers: Proceedings of the 8th International Conference on Signal and Information Processing, Networking and Computers (ICSINC), 2022, pp. 1492-1500.

F. Wang, D. Jiang, H. Wen, and H. Song, "Adaboost-based security level classification of mobile intelligent terminals," The Journal of Supercomputing, vol. 75, pp. 7460-7478, 2019.