Critical Review on Machine Learning in 5G Mobile Networks

Authors

  • Mohamed Mohsen Farouk Faculty of Engineering, Multimedia University – 63100, Malaysia
  • Wai Leong Pang Faculty of Engineering, Multimedia University – 63100, Malaysia
  • Gwo Chin Chung Faculty of Engineering, Multimedia University – 63100, Malaysia
  • Mardeni Roslee Faculty of Engineering, Multimedia University – 63100, Malaysia

Keywords:

5G, Network Slicing, QoS, Reinforcement Learning

Abstract

The 5G wireless network standard was created through the 3rd Generation Partnership Project as the next big leap in wireless network technology. Offering several advantages over previous generations of wireless network technology 5G, promises to bring with it higher speeds, lower latency rates, better reliability, and much more. All these improvements offered by the Fifth-Generation network allowed for a variety of new applications that increased the complexity of maintaining the quality of service across the network due to their stringent and heterogeneous quality of service requirements, which must be met simultaneously. This increased complexity has motivated many researchers to propose innovative approaches in the literature, such as implementing network schedulers that can leverage different kinds of machine learning, network slicing, or combinations of several different techniques. This has motivated us to investigate the existing research regarding machine learning in network orchestration, to determine the most capable methods and algorithms to satisfy the quality-of-service requirements of the heterogeneous traffic flows across the 5G wireless network.

Downloads

Download data is not yet available.

References

M. E. Ezhilarasan, "A Review on mobile technologies: 3G, 4G and 5G," in 2017 Second International Conference on Recent Trends and Challenges in Computational Models (ICRTCCM), Tindivanam, 2017.

J. Navarro-Ortiz, P. Romero-Diaz, S. Sendra, P. Ameigeiras, J. J. Ramos-Munoz and J. M. Lopez-Soler, "A Survey on 5G Usage Scenarios," IEEE Communications Surveys & Tutorials, vol. 22, no. 2, pp. 905-929, 2020.

H. Zhou, M. Elsayed and M. E.-K. , "RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning," in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), London, 2021.

M. Polese, R. Jana, V. Kounev, K. Z. and M. Zorzi, "Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks," IEEE Transactions on Mobile Computing, vol. 20, no. 12, pp. 3367-3382, 2020.

S. Mollahasani, M. Erol-Kantarci, M. Hirab, H. Dehgan and R. Wilson, "Actor-Critic Learning Based QoS-Aware Scheduler," IEEE Transactions on Network Science and Engineering, vol. 9, no. 1, pp. 45-54, 2021.

M. A. Kamal, H. W. Raza, M. M. Alam, M. M. Su'ud and A. b. A. B. Sajak, "Resource Allocation Schemes for 5G Network: A Systematic Review," Sensors, vol. 21, no. 19, p. 6588, 2021.

H. Beshley, M. Beshley, M. Medvetskyi and J. Pyrih, "QoS-Aware Optimal Radio Resource Allocation Method for Machine-Type Communications in 5G LTE and beyond Cellular Networks," Hindawi Wireless Communications and Mobile Computing, vol. 2021, 2021.

N. Abbou, T. Taleb and J. Song, "A Software-Defined Queuing Framework for QoS Provisioning in 5G and Beyond Mobile," IEEE Network, vol. 35, no. 2, pp. 168-173, 2021.

M. Raftopoulou and R. Litjens, "Optimisation of Numerology and Packet Scheduling in 5G Networks: To Slice or not to Slice?," in 2021 IEEE 93rd Vehicular Technology Conference, Helsinki, 2021.

S. Parkvall, E. Dahlman, A. Furuskar and M. Frenne, "NR: The New 5G Radio Access Technology," IEEE Communications Standards Magazine, vol. 1, no. 4, pp. 24-30, 2017.

Jing Hong Wai, Ying Loong Lee and Feng Ke, "Combined Metric-Based Resource Scheduling for 5G Networks," in IEEE 15th Malaysia International Conference on Communication (MICC), 2021.

C. Janiesch, P. Zschech and K. Heinrich, "Machine learning and deep learning," Electron Markets, vol. 31, p. 685–695, 2021.

M. H. Abidi, H. Alkhalefah, K. Moiduddin, M. Alazab, M. K. Mohammed, W. Ameen and T. R. Gadekallu, "Optimal 5G network slicing using machine learning and deep learning concepts," Computer Standards & Interfaces, vol. 76, 2021.

N. Salhab, R. Langar and R. Rahim, "5G network slices resource orchestration using Machine Learning techniques," Computer Networks, vol. 188, 2021.

Prasad, A. K. ., M, D. K. ., Macedo, V. D. J. ., Mohan, B. R. ., & N, A. P. . (2023). Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1s), 43–51. https://doi.org/10.17762/ijritcc.v11i1s.5992

Mr. Nikhil Surkar, Ms. Shriya Timande. (2012). Analysis of Analog to Digital Converter for Biomedical Applications. International Journal of New Practices in Management and Engineering, 1(03), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/6

Downloads

Published

11.07.2023

How to Cite

Farouk, M. M. ., Pang, W. L. ., Chung, G. C. ., & Roslee, M. . (2023). Critical Review on Machine Learning in 5G Mobile Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 362–367. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3060

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.