Open Access Open Access  Restricted Access Subscription Access

Comparative Study of Machine Learning Techniques for Intelligent 6G Wireless Networks

Anita Patil, Sridhar Iyer


With exponential increase in the traffic and corresponding bandwidth demand, there is an immediate requirement to serve this traffic through the high-speed wireless communication networks. To fulfil requirements of the next-generation users, it will be mandatory to resort to use of advanced physical layer solutions, spectral bands at higher frequency, and efficient software enabled intelligent algorithms. In this regard, Machine Learning (ML) algorithms will play a key role in ensuring intelligence within 6G wireless system. In this article, we present a detailed review of the ML techniques which are applicable to the next generation 6G wireless networks. We also highlight the most attractive applications of 6G networks, and conclude by mentioning the importance of adopting ML within the 6G networks.


6G, intelligence, machine learning, wireless networks.

Full Text:



S. Ali, W. Saad, N. Rajatheva, et al.,”6G White Paper on Machine Learning in Wireless Communication Networks”, arXiv:2004.13875v1 [cs.IT], 2020.

M.W. Libbrecht, and W.S. Noble, “Machine learning applications in genetics and genomics”, Nature Reviews Genetics, vol. 16, no. 6, 2015.

Big Data Made Simple (5 Feb, 2018). Machine learning explained: Understanding supervised, unsupervised, and reinforcement learning [Online]. Available from

Technative (9 Sep. 2021). Why Unsupervised Machine Learning is the Future of Cybersecurity [Online]. Available from

T. Hofmann, “Unsupervised Learning by Probabilistic Latent Semantic Analysis”, Machine Learning, vol. 42, no. 1-2, pp. 177-196, 2001.

Y. Zhao, W. Zhai, J. Zhao, T. Zhang, S. Sun, D. Niyato, and K. Yan Lam “A Comprehensive Survey of 6G Wireless Communications” arXiv:2101.03889v2 [eess.SP], 2021.

Techvidvan. Reinforcement Learning Algorithms and Applications (online). Available from

J. Kaur, M. Arif Khan, M. Iftikhari, M. Imran, and Q. Emad Ul Haq,” Machine Learning Techniques for 5G and Beyond”, IEEE ACCESS, vol. 9, pp. 23472 – 23488, 2021.

Medium (19 May, 2020). Instilling Responsible and Reliable AI Development with Federated Learning [Online]. Available from

G. Ghosh, P. Das, and S. Chatterjee, “Cognitive Radio and Dynamic Spectrum Access – A Study”, International Journal of next Generation networks, vol. 6, no. 1, pp. 43-60, 2014.

X. Hong, J. Wang, J. Shi, “Cognitive radio in 5G: a perspective on energy-spectral efficiency trade–off”, IEEE Communications Magazine, vol. 52, no. 7, pp. 46-63, 2014.

T. Hewa, G. Gurkan, A. Kalla, M. Ylianttila et al., “The Role of Blockchain in 6G: Challenges, Opportunities and Research Directions”, in 2020 2nd 6G Wireless Summit, 2020.

P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza, “A survey of machine learning techniques applied to self-organizing cellular networks”, IEEE Commun. Surveys Tuts., vol. 19, no. 4, pp. 2392–2431, 2017.

F. Azmat, Y. Chen, and N. Stocks, “Predictive modelling of RF energy for wireless powered communications,’’ IEEE Commun. Lett., vol. 20, no. 1, pp. 173–176, Jan. 2016.

W. Guo, “Explainable artificial intelligence (XAI) for 6G: Improving trust between human and machine,” arXiv:1911.04542, 2019.

A. Martin, J. Egana, J. Florez, J. Montalban, I. G. Olaizola, M. Quartulli, R. Viola, and M. Zorrilla, ‘‘Network resource allocation system for QoE-aware delivery of media services in 5G networks”, IEEE Trans. Broadcast., vol. 64, no. 2, pp. 561–574, Jun. 2018.

J. Liu, R. Deng, S. Zhou, and Z. Niu, ‘‘Seeing the unobservable: Channel learning for wireless communication networks”, in Proc. IEEE Global Commun. Conf. (GLOBECOM), San Diego, CA, USA, Dec. 2014, pp. 1–6.

S. J. Nawaz, S. K. Sharma, S. Wyne, M. N. Patwary, and M. Asaduzzaman, “Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future,’’ IEEE Access, vol. 7, pp. 46317–46350, 2019.

C. Sun and C. Yang, ‘‘Learning to optimize with unsupervised learning: Training deep neural networks for URLLC,’’ in Proc. IEEE 30th Annu. Int. Symp. Pers., Indoor Mobile Radio Commun. (PIMRC), Istanbul, Turkey, Sep. 2019, pp. 1–7.

M. S. Parwez, D. B. Rawat, and M. Garuba, ‘‘Big data analytics for user activity analysis and user-anomaly detection in mobile wireless network,’’ IEEE Trans. Ind. Informat., vol. 13, no. 4, pp. 2058–2065, Aug. 2017.

E. Balevi and R. D. Gitlin, ‘‘Unsupervised machine learning in 5G networks for low latency communications,’’ in Proc. IEEE 36th Int. Perform. Comput. Commun. Conf. (IPCCC), San Diego, CA, USA, Dec. 2017, pp. 1–2.

J. Gazda, E. Slapak, G. Bugar, D. Horvath, T. Maksymyuk, and M. Jo, ‘‘Unsupervised learning algorithm for intelligent coverage planning and performance optimization of multitier heterogeneous network,’’ IEEE Access, vol. 6, pp. 39807–39819, 2018.

A. T. Z. Kasgari and W. Saad, ‘‘Model-free ultra-reliable low latency communication (URLLC): A deep reinforcement learning framework,’’ in Proc. IEEE Int. Conf. Commun. (ICC), Shanghai, China, May 2019, pp. 1–6.

Y. Ling, B. Yi, and Q. Zhu, ‘‘An improved vertical handoff decision algorithm for heterogeneous wireless networks,’’ in Proc. 4th Int. Conf. Wireless Commun., Netw. Mobile Comput., Dalian, China, Oct. 2008, pp. 1–3.

K. Hamidouche, A. T. Z. Kasgari, W. Saad, M. Bennis, and M. Debbah, ‘‘Collaborative artificial intelligence (AI) for user-cell association in ultra-dense cellular systems,’’ in Proc. IEEE Int. Conf. Commun. Workshops (ICC Workshops), Kansas City, MO, USA, May 2018, pp. 1–6.

L. T. Tan and R. Q. Hu, ‘‘Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning,’’ IEEE Trans. Veh. Technol., vol. 67, no. 11, pp. 10190–10203, Nov. 2018.

J. Konecny, H.B. McMahan, Felix X. Yu, A. Theertha Suresh, and D. Bacon, “Federated Learning: Strategies for Improving Communication Efficiency”, 2020.

A. Salh, L. Audah, NSM. Shah et al., “A Survey on Deep Learning for Ultra-Reliable and Low-Latency Communications Challenges on 6G Wireless Systems”, in the Proceedings of Future of Information and Communication Conference (FICC) 2021, arXiv:2004.08549v3 [eess.SP], 2020.

M.Z. Chowdhury, MD. Shahjalal, S. Ahmed, Y. Min Jang, “6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions”, IEEE Open Journal of the Communications Society, vol. 1, pp. 957 – 975, 2020.

Digital Vidya. Supervised learning [online]. Available from



  • There are currently no refbacks.

Copyright (c) 2022 Journal of Communication Engineering & Systems