Artificial Intelligence in 5G Technology: Overview of System Models
DOI:
https://doi.org/10.18034/apjee.v8i1.595Keywords:
Spectrum Selection, IoT, Artificial Intelligence, 5G Technology, AI-based InterfaceAbstract
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
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--