Artificial Intelligence in 5G Technology: Overview of System Models

Authors

  • Md. Mostafijur Rahman FCUB
  • Mani Manavalan Capgemini America, Inc
  • Taposh Kumar Neogy Rajshahi University

DOI:

https://doi.org/10.18034/apjee.v8i1.595

Keywords:

Spectrum Selection, IoT, Artificial Intelligence, 5G Technology, AI-based Interface

Abstract

The occurrence of various devices that are interlinked to provide advanced connectivity throughout the systems revolves around the formation of 5G systems. Artificial Intelligence plays a fundamental role in the 5G networks. The popularity and integration of 5G have emerged through advanced cellular networks and many other technologies. This innovative and speedy network has built strong connections in recent years, its conduct in business, personal work, or daily life. Artificial Intelligence and edge computing devices have optimized internet usages in everyday life. The growth of 5G networks is effective in the AI/ML algorithms due to its low latency and high bandwidth, which also performs real-time analysis, reasoning, and optimization. The 5G era has fundamental features that are highlighted among the revolutionary techniques which are most commonly used by cellular device networks, such as the resource management of radio, mobility management, and service management, and so on. This work also integrates the selection of spectrum and access the spectrum which AI-based interface to accomplish demands of 5G. The strategies which are introduced are Fractional Knapsack Greedy-based strategy and Language Hyperplane approach which becomes the basis of subsequently utilized by strategies of Artificial Intelligence for purpose of the selection of spectrum and the right allocation of spectrum for IoT-enabled sensor networks.

 

Downloads

Download data is not yet available.

Author Biographies

  • Md. Mostafijur Rahman, FCUB

    Lecturer,  Department of Business Administration, First Capital University of Bangladesh, Alamdanga Road, Chuadanga-7200, BANGLADESH

  • Mani Manavalan, Capgemini America, Inc

    Sr. Architect, Capgemini America, Inc., United States

  • Taposh Kumar Neogy, Rajshahi University

    Ph.D., Department of Accounting and Information Systems (AIS), University of Rajshahi, Rajshahi – 6205, BANGLADESH

References

Ahmed, A. A. A., & Ganapathy, A. (2021). Creation of Automated Content with Embedded Artificial Intelligence: A Study on Learning Management System for Educational Entrepreneurship. Academy of Entrepreneurship Journal, 27(3), 1-10, https://doi.org/10.5281/zenodo.4973057

Ahmed, A. A. A., Aljarbouh, A., Donepudi, P. K., & Choi, M. S. (2021). Detecting Fake News using Machine Learning: A Systematic Literature Review. Psychology and Education, 58(1), 1932–1939. https://doi.org/10.17762/pae.v58i1.1046

Amin, R., & Manavalan, M. (2017). Modeling Long Short-Term Memory in Quantum Optical Experiments. International Journal of Reciprocal Symmetry and Physical Sciences, 4, 6–13. Retrieved from https://upright.pub/index.php/ijrsps/article/view/48

Azmat, F., Chen, Y., Stocks, N. (2016). Analysis of Spectrum Occupancy Using Machine Learning Algorithms. IEEE Transactions on Vehicular Technology, 65(9), 6853 - 6860. https://doi.org/10.1109/TVT.2015.2487047

Bynagari, N. B. & Ahmed, A. A. A. (2021). Anti-Money Laundering Recognition through the Gradient Boosting Classifier. Academy of Accounting and Financial Studies Journal, 25(5), 1–11. https://doi.org/10.5281/zenodo.5523918

Bynagari, N. B. (2015). Machine Learning and Artificial Intelligence in Online Fake Transaction Alerting. Engineering International, 3(2), 115-126. https://doi.org/10.18034/ei.v3i2.566

Bynagari, N. B. (2019). GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. Asian Journal of Applied Science and Engineering, 8, 25–34. Retrieved from https://upright.pub/index.php/ajase/article/view/32

Bynagari, N. B., & Amin, R. (2019). Information Acquisition Driven by Reinforcement in Non-Deterministic Environments. American Journal of Trade and Policy, 6(3), 107-112. https://doi.org/10.18034/ajtp.v6i3.569

Doewes, R. I.; Ahmed, A. A. A.; Bhagat, A.; Nair, R.; Donepudi, P. K.; Goon, S.; Jain, V.; Gupta, S.; Rathore, N. K.; Jain, N. K. (2021). A regression analysis based system for sentiment analysis and a method thereof. Australian Official Journal of Patents, 35(17), Patent number: 2021101792. https://lnkd.in/gwsbbXa

Fadziso, T., & Manavalan, M. (2017). Identical by Descent (IBD): Investigation of the Genetic Ties between Africans, Denisovans, and Neandertals. Asian Journal of Humanity, Art and Literature, 4(2), 157-170. https://doi.org/10.18034/ajhal.v4i2.582

Fan, C., Li, B., Zhao, C., Guo W. and Liang, YC. (2018). Learning-Based Spectrum Sharing and Spatial Reuse in mm-Wave Ultradense Networks. IEEE Transactions on Vehicular Technology, 67(6), 4954-4968. https://doi.org/10.1109/TVT.2017.2750801

Huang, Y., Tan, J. & Liang, YC. (2017). Wireless big data: transforming heterogeneous networks to smart networks. Journal of Communications and Information Networks. 2, 19–32. https://doi.org/10.1007/s41650-017-0002-1

I, CL., Han, S., Xu, Z., Wang, S., Sun, Q., Chen, Y. (2016). New Paradigm of 5G Wireless Internet. IEEE Journal on Selected Areas in Communications, 34(3), 474-482. https://doi.org/10.1109/JSAC.2016.2525739

Li, R., Zhao, Z., Zhou, X., Palicot, J. and Zhang, H. (2014). The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice. IEEE Communications Magazine, 52(6), 234-240. https://doi.org/10.1109/MCOM.2014.6829969

Li, Z. and Guo, C. (2020). Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications. IEEE Transactions on Vehicular Technology, 69(2), 1828-1840. https://doi.org/10.1109/TVT.2019.2961405

Li, Z. and Guo, C. (2019). Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications. Networking and Internet Architecture. https://arxiv.org/abs/1912.09302

Lin, K., Li, C., Tian, D., Ghoneim, A., Hossain, M. S., Amin, S. U. (2019). Artificial-Intelligence-Based Data Analytics for Cognitive Communication in Heterogeneous Wireless Networks. IEEE Wireless Communications, 26(3), 83-89. https://doi.org/10.1109/MWC.2019.1800351

Manavalan, M. (2019a). P-SVM Gene Selection for Automated Microarray Categorization. International Journal of Reciprocal Symmetry and Physical Sciences, 6, 1–7. Retrieved from https://upright.pub/index.php/ijrsps/article/view/43

Manavalan, M. (2019b). Using Fuzzy Equivalence Relations to Model Position Specificity in Sequence Kernels. Asian Journal of Applied Science and Engineering, 8, 51–64. Retrieved from https://upright.pub/index.php/ajase/article/view/42

Manavalan, M. (2020). Intersection of Artificial Intelligence, Machine Learning, and Internet of Things – An Economic Overview. Global Disclosure of Economics and Business, 9(2), 119-128. https://doi.org/10.18034/gdeb.v9i2.584

Manavalan, M., & Chisty, N. M. A. (2019). Visualizing the Impact of Cyberattacks on Web-Based Transactions on Large-Scale Data and Knowledge-Based Systems. Engineering International, 7(2), 95-104. https://doi.org/10.18034/ei.v7i2.578

Sharma, D. K., Chakravarthi, D. S., Shaikh, A. A., Ahmed, A. A. A., Jaiswal, S., Naved, M. (2021). The aspect of vast data management problem in healthcare sector and implementation of cloud computing technique. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.07.388

Soldani, D. and Manzalini, A. (2015). Horizon 2020 and Beyond: On the 5G Operating System for a True Digital Society. IEEE Vehic. Tech. Mag., 10(1), 32–42.

Song, H., Bai, J., Yi, Y., Wu, J. and Liu, L. (2020). Artificial Intelligence Enabled Internet of Things: Network Architecture and Spectrum Access. IEEE Computational Intelligence Magazine, 15(1), 44-51. https://doi.org/10.1109/MCI.2019.2954643

Wang, J., Jiang, C., Zhang, H., Ren, Y., Chen, KC. and Hanzo, L. (2020). Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks. IEEE Communications Surveys & Tutorials, 22(3), 1472-1514. https://doi.org/10.1109/COMST.2020.2965856

Wang, X., Li, X., and Leung, V. C. M. (2015). Artificial Intelligence-Based Techniques for Emerging Heterogeneous Network: State of the Arts, Opportunities, and Challenges. IEEE Access, 3, 1379–1391.

Yao, M., Sohul, M., Marojevic, V. and Reed, J. H. (2019). Artificial Intelligence Defined 5G Radio Access Networks. IEEE Communications Magazine, 57(3), 14-20. https://doi.org/10.1109/MCOM.2019.1800629

Zhou, X., Li, R., Chen, T., Zhang, H. (2016). Network slicing as a service: enabling enterprises' own software-defined cellular networks. IEEE Communications Magazine, 54(7), 146-153. https://doi.org/10.1109/MCOM.2016.7509393

--0--

Downloads

Published

2021-03-05

How to Cite

Rahman, M. M., Manavalan, M. ., & Neogy, T. K. (2021). Artificial Intelligence in 5G Technology: Overview of System Models. Asia Pacific Journal of Energy and Environment, 8(1), 7-16. https://doi.org/10.18034/apjee.v8i1.595