Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 12, 17 November 2023
Open
Access | Article
Application of machine learning in wireless communication
* Author to whom correspondence should be addressed.
Abstract
With the rapid development of wireless communication technology, people have put forward higher requirements for the speed and quality of data transmission in wireless communication while enjoying a convenient life. Wireless communication systems are expected to be combined with artificial intelligence to meet these requirements. Machine learning (ML) can rely on different algorithms to process data without explicit programming. It can also optimize wireless systems by solving complex problems that traditional mathematics cannot solve. This paper briefly introduces wireless communication, machine learning, and the necessity of combining machine learning. The potential and applications of machine learning in various aspects of wireless communication, such as channel estimation, spectrum allocation, adaptive interference suppression, etc., are listed. The paper also introduces the various conveniences that machine learning in wireless communication brings to people in practical applications and the potential hazards that improper applications may bring.
Keywords
Wireless Communication, Machine Learning, Application Situation
References
1. W. Chin, Z. Fan, and R. Haines, 'Emerging technologies and research challenges for 5G wireless networks', IEEE Wireless Commun., vol. 21, no. 2, pp. 106–112, Apr. 2014, doi: 10.1109/MWC.2014.6812298.
2. Y. Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, 'A Survey on Mobile Edge Computing: The Communication Perspective'. arXiv, Jun. 13, 2017. Accessed: Jul. 05, 2023. [Online]. Available: http://arxiv.org/abs/1701.01090
3. Y. Sun, M. Peng, Y. Zhou, Y. Huang, and S. Mao, 'Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues'. arXiv, Feb. 28, 2019. Accessed: Jul. 05, 2023. [Online]. Available: http://arxiv.org/abs/1809.08707
4. M. A. Alsheikh, S. Lin, D. Niyato, and H.-P. Tan, 'Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications', IEEE Commun. Surv. Tutorials, vol. 16, no. 4, pp. 1996–2018, 2014, doi: 10.1109/COMST.2014.2320099.
5. C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, 'Machine Learning Paradigms for Next-Generation Wireless Networks', IEEE Wireless Commun., vol. 24, no. 2, pp. 98–105, Apr. 2017, doi: 10.1109/MWC.2016.1500356WC.
6. M. Bkassiny, Y. Li, and S. K. Jayaweera, 'A Survey on Machine-Learning Techniques in Cognitive Radios', IEEE Commun. Surv. Tutorials, vol. 15, no. 3, pp. 1136–1159, 2013, doi: 10.1109/SURV.2012.100412.00017.
7. R. Li, Z. Zhao, X. Chen, J. Palicot, and H. Zhang, 'TACT: A Transfer Actor-Critic Learning Framework for Energy Saving in Cellular Radio Access Networks', IEEE Trans. Wireless Commun., vol. 13, no. 4, pp. 2000–2011, Apr. 2014, doi: 10.1109/TWC.2014.022014.130840.
8. T. Park, N. Abuzainab, and W. Saad, 'Learning How to Communicate in the Internet of Things: Finite Resources and Heterogeneity', IEEE Access, vol. 4, pp. 7063–7073, 2016, doi: 10.1109/ACCESS.2016.2615643.
9. T. J. O’Shea and J. Hoydis, ‘An Introduction to Deep Learning for the Physical Layer’. arXiv, Jul. 11, 2017. Accessed: Jul. 06, 2023. [Online]. Available: http://arxiv.org/abs/1702.00832
10. H. Peng and X. Shen, ‘Deep Reinforcement Learning Based Resource Management for Multi-Access Edge Computing in Vehicular Networks’, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 4, pp. 2416–2428, Oct. 2020, doi: 10.1109/TNSE.2020.2978856.
11. X. Shen et al., ‘AI-Assisted Network-Slicing Based Next-Generation Wireless Networks’, IEEE Open J. Veh. Technol., vol. 1, pp. 45–66, 2020, doi: 10.1109/OJVT.2020.2965100.
12. M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, ‘Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial’, IEEE Commun. Surv. Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019, doi: 10.1109/COMST.2019.2926625.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-135-3
- ISBN (Online)
- 978-1-83558-136-0
- Published Date
- 17 November 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/12/20230452
- Copyright
- 17 November 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited