USING ML FOR IMPROVING THE EFFICIENCY AND RELIABILITY OF 6G WIRELESS NETWORKS

Hamid Jafarabad1, Majid fouladian2* and Seyed Mohammad Jalal Rastegar Fatemi3

Abstract

This Paper presents the potential role of machine learning (ML) in the development and implementation of 6G wireless communications. ML techniques can be used for various applications such as intelligent resource management, ultra-reliable and low-latency communications, advanced sensing and perception, quantum machine learning, security, self-organizing networks, and edge computing. In addition to these applications, ML can also address challenges in different layers of the 6G wireless network, including the physical, medium-access, and application layers. Furthermore, zero-touch optimization using ML can automate network optimization processes without human intervention, resulting in improved network performance and efficiency while reducing operational costs. Tasks that can be automated through zero-touch optimization using ML include resource allocation, network slicing, and fault management. Overall, the integration of ML into 6G wireless communications has the potential to significantly enhance network performance, intelligence, and reliability, paving the way for a new era of wireless connectivity.

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[1] Ali, S., Saad, W., Rajatheva, N., Chang, K., Steinbach, D., Sliwa, B., ... & Malik, H. (2020). 6G white paper on machine learning in wireless communication networks. arXiv preprint arXiv:2004.13875. [2] Sharma, T., Chehri, A., & Fortier, P. (2021). Review of optical and wireless backhaul networks and emerging trends of next generation 5G and 6G technologies. Transactions on Emerging Telecommunications Technologies, 32(3), e4155. [3] Qi, Q., & Tao, F. (2019). A smart manufacturing service system based on edge computing, fog computing, and cloud computing. IEEE access, 7, 86769-86777. [4] Sharma, P., Jain, S., Gupta, S., & Chamola, V. (2021). Role of machine learning and deep learning in securing 5G-driven industrial IoT applications. Ad Hoc Networks, 123, 102685. [5] Tan, K., Bremner, D., Le Kernec, J., Zhang, L., & Imran, M. (2022). Machine learning in vehicular networking: An overview. Digital Communications and Networks, 8(1), 18-24. [6] ElSawy, H., Hossain, E., & Haenggi, M. (2013). Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey. IEEE Communications Surveys & Tutorials, 15(3), 996-1019. [7] Wang, R., Peng, X., Zhang, J., & Letaief, K. B. (2016). Mobility-aware caching for content-centric wireless networks: Modeling and methodology. IEEE Communications Magazine, 54(8), 77-83. [8] Coker, E. S., Amegah, A. K., Mwebaze, E., Ssematimba, J., & Bainomugisha, E. (2021). A land use regression model using machine learning and locally developed low cost particulate matter sensors in Uganda. Environmental Research, 199, 111352. [9] Ali, S., Saad, W., Rajatheva, N., Chang, K., Steinbach, D., Sliwa, B., ... & Malik, H. (2020). 6G white paper on machine learning in wireless communication networks. arXiv preprint arXiv:2004.13875. [10] Yang, Z., Chen, M., Wong, K. K., Poor, H. V., & Cui, S. (2022). Federated learning for 6G: Applications, challenges, and opportunities. Engineering, 8, 33-41. [11] Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020). Federated learning for 6G communications: Challenges, methods, and future directions. China Communications, 17(9), 105-118. [12] Salameh, H. B., Masadeh, A. E., & El Refae, G. (2022). Intelligent drone-base-station placement for improved revenue in b5g/6g systems under uncertain fluctuated demands. IEEE Access, 10, 106740-106749. [13] Gopi, S. P., & Magarini, M. (2021). Reinforcement learning aided uav base station location optimization for rate maximization. Electronics, 10(23), 2953. [14] Schmidt, J., Marques, M. R., Botti, S., & Marques, M. A. (2019). Recent advances and applications of machine learning in solid-state materials science. npj Computational Materials, 5(1), 83. [15] Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019). Recent progress on generative adversarial networks (GANs): A survey. IEEE access, 7, 36322-36333. [16] Zhu, Z., Lin, K., Jain, A. K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning: A survey. arXiv preprint arXiv:2009.07888. [17] Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., ... & He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76. [18] Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4, 51-62. [19] Li, N., Shepperd, M., & Guo, Y. (2020). A systematic review of unsupervised learning techniques for software defect prediction. Information and Software Technology, 122, 106287. [20] François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11(3-4), 219-354. [21] Zamir, A. R., Sax, A., Shen, W., Guibas, L. J., Malik, J., & Savarese, S. (2018). Taskonomy: Disentangling task transfer learning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3712-3722). [22] Shinde, P. P., & Shah, S. (2018, August). A review of machine learning and deep learning applications. In 2018 Fourth international conference on computing communication control and automation (ICCUBEA) (pp. 1-6). IEEE. [23] Alsharif, M. H., Kelechi, A. H., Albreem, M. A., Chaudhry, S. A., Zia, M. S., & Kim, S. (2020). Sixth generation (6G) wireless networks: Vision, research activities, challenges and potential solutions. Symmetry, 12(4), 676. [24] Khiadani, N. (2020, December). Vision, requirements and challenges of sixth generation (6G) networks. In 2020 6th Iranian conference on signal processing and intelligent systems (ICSPIS) (pp. 1-4). IEEE. [25] Chowdhury, M. Z., Shahjalal, M., Ahmed, S., & Jang, Y. M. (2020). 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions. IEEE Open Journal of the Communications Society, 1, 957-975. [26] Gawas, A. U. (2015). An overview on evolution of mobile wireless communication networks: 1G-6G. International Journal on Recent and Innovation Trends in Computing and Communication, 3(5), 3130-3133. [27] Iliev, T. B., Ivanova, E. P., Stoyanov, I. S., Mihaylov, G. Y., & Beloev, I. H. (2021, September). Artificial Intelligence in Wireless Communications-Evolution Towards 6G Mobile Networks. In 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 432-437). IEEE. [28] Lee, W., Jo, O., & Kim, M. (2020). Intelligent resource allocation in wireless communications systems. IEEE Communications Magazine, 58(1), 100-105. [29] Yeo, J. C., Liu, Z., Zhang, Z. Q., Zhang, P., Wang, Z., & Lim, C. T. (2017). Wearable mechanotransduced tactile sensor for haptic perception. Advanced Materials Technologies, 2(6), 1700006. [30] Lin, H. L., Liao, P. K., & Wu, W. D. (2018). U.S. Patent Application No. 15/835,768. [31] O'brien, J. L. (2007). Optical quantum computing. Science, 318(5856), 1567-1570. [32] Ziegler, V., & Yrjola, S. (2020, March). 6G indicators of value and performance. In 2020 2nd 6G wireless summit (6G SUMMIT) (pp. 1-5). IEEE. [33] Zhao, Z., Du, Q., Wang, D., Tang, X., & Song, H. (2022). Overview of prospects for service-aware radio access towards 6g networks. Electronics, 11(8), 1262. [34] Muscinelli, E., Shinde, S. S., & Tarchi, D. (2022). Overview of distributed machine learning techniques for 6G networks. Algorithms, 15(6), 210. [35] Rodrigues, T. K., Liu, J., & Kato, N. (2021). Application of cybertwin for offloading in mobile multiaccess edge computing for 6G networks. IEEE Internet of Things Journal, 8(22), 16231-16242. [36] Vaigandla, K. K. (2022, February). Communication Technologies and Challenges on 6G Networks for the Internet: Internet of Things (IoT) Based Analysis. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (Vol. 2, pp. 27-31). IEEE. [37] Nawaz, F., Ibrahim, J., Muhammad, A. A., Junaid, M., Kousar, S., & Parveen, T. (2020). A review of vision and challenges of 6G technology. International Journal of Advanced Computer Science and Applications, 11(2). [38] Rahman, M. A., & Hossain, M. S. (2022). A deep learning assisted software defined security architecture for 6G wireless networks: IIoT perspective. IEEE Wireless Communications, 29(2), 52-59. [39] Hodge, J. A., Mishra, K. V., & Zaghloul, A. I. (2020). Intelligent time-varying metasurface transceiver for index modulation in 6G wireless networks. IEEE Antennas and Wireless Propagation Letters, 19(11), 1891-1895. [40] Khowaja, S. A., Dev, K., Khowaja, P., & Bellavista, P. (2021). Toward energy-efficient distributed federated learning for 6G networks. IEEE Wireless Communications, 28(6), 34-40. [41] Catak, E., Catak, F. O., & Moldsvor, A. (2021, May). Adversarial machine learning security problems for 6G: mmWave beam prediction use-case. In 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom) (pp. 1-6). IEEE. [42] Yin, L., Yang, R., & Yao, Y. (2021). Channel Sounding and Scene Classification of Indoor 6G Millimeter Wave Channel Based on Machine Learning. Electronics, 10(7), 843. [43] Rasti, M., Taskou, S. K., Tabassum, H., & Hossain, E. (2022). Evolution toward 6g multi-band wireless networks: A resource management perspective. IEEE Wireless Communications, 29(4), 118-125. [44] Imanbayev, A., Tynymbayev, S., Odarchenko, R., Gnatyuk, S., Berdibayev, R., Baikenov, A., & Kaniyeva, N. (2022). Research of Machine Learning Algorithms for the Development of Intrusion Detection Systems in 5G Mobile Networks and Beyond. Sensors, 22(24), 9957. [45] Wikström, G., Peisa, J., Rugeland, P., Johansson, N., Parkvall, S., Girnyk, M., ... & Da Silva, I. L. (2020, March). Challenges and technologies for 6G. In 2020 2nd 6G wireless summit (6G SUMMIT) (pp. 1-5). IEEE. [46] Giordani, M., Polese, M., Mezzavilla, M., Rangan, S., & Zorzi, M. (2020). Toward 6G networks: Use cases and technologies. IEEE Communications Magazine, 58(3), 55-61. [47] David, K., Elmirghani, J., Haas, H., & You, X. H. (2019). Defining 6G: Challenges and opportunities [from the guest editors]. IEEE Vehicular Technology Magazine, 14(3), 14-16. [48] Bharathi, S., & Durgadevi, P. (2022, May). A Comprehensive Investigation on Role of Machine Learning in 6G Technology. In Proceedings of International Conference on Communication and Artificial Intelligence: ICCAI 2021 (pp. 35-47). Singapore: Springer Nature Singapore. [49] SUMAIYA, N., & ALSEKAIT, D. M. (2022). Machine Learning Based Industrial Engineering With 6G Technology. Journal of Pharmaceutical Negative Results, 13. [50] Singh, P., Agrawal, R., & Singh, K. K. (2023). Maximizing user retention with machine learning enabled 6G channel allocation. International Journal of Information Technology, 1-7. [51] Xu, J. W., Paiva, A. R., Park, I., & Principe, J. C. (2008). A reproducing kernel Hilbert space framework for information-theoretic learning. IEEE Transactions on Signal Processing, 56(12), 5891-5902. [52] Bertsimas, D., & Koduri, N. (2022). Data-driven optimization: A reproducing kernel Hilbert space approach. Operations Research, 70(1), 454-471. [53] Deng, L. J., Guo, W., & Huang, T. Z. (2015). Single-image super-resolution via an iterative reproducing kernel Hilbert space method. IEEE Transactions on Circuits and Systems for Video Technology, 26(11), 2001-2014. [54] Zhao, B., Cheng, C., Tu, G., Peng, Z., He, Q., & Meng, G. (2021). An interpretable denoising layer for neural networks based on reproducing kernel Hilbert space and its application in machine fault diagnosis. Chinese Journal of Mechanical Engineering, 34(1), 1-11. [55] Rasti, M., Taskou, S. K., Tabassum, H., & Hossain, E. (2022). Evolution toward 6g multi-band wireless networks: A resource management perspective. IEEE Wireless Communications, 29(4), 118-125. [56] Wang, J., Ling, X., Le, Y., Huang, Y., & You, X. (2021). Blockchain-enabled wireless communications: a new paradigm towards 6G. National science review, 8(9), nwab069. [57] Iyer, S., Pandya, R. J., Kallimani, R., Pai, K., Khanai, R., Torse, D., & Mavinkattimath, S. (2022). Survey on Internet of Things enabled by 6G Wireless Networks. arXiv preprint arXiv:2203.08426. [58] Liu, Y., Yuan, X., Xiong, Z., Kang, J., Wang, X., & Niyato, D. (2020). Federated learning for 6G communications: Challenges, methods, and future directions. China Communications, 17(9), 105-118. [59] Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., & Gao, Y. (2021). A survey on federated learning. Knowledge-Based Systems, 216, 106775. [60] Fadlullah, Z. M., & Kato, N. (2020). HCP: Heterogeneous computing platform for federated learning based collaborative content caching towards 6G networks. IEEE Transactions on Emerging Topics in Computing, 10(1), 112-123. [61] Li, L., Fan, Y., Tse, M., & Lin, K. Y. (2020). A review of applications in federated learning. Computers & Industrial Engineering, 149, 106854. [62] Al-Rawi, H. A., Ng, M. A., & Yau, K. L. A. (2015). Application of reinforcement learning to routing in distributed wireless networks: a review. Artificial Intelligence Review, 43, 381-416. [63] Gong, Y., Yao, H., Wang, J., Jiang, L., & Yu, F. R. (2021). Multi-agent driven resource allocation and interference management for deep edge networks. IEEE Transactions on Vehicular Technology, 71(2), 2018-2030. [64] Yang, H., Alphones, A., Xiong, Z., Niyato, D., Zhao, J., & Wu, K. (2020). Artificial-intelligence-enabled intelligent 6G networks. IEEE Network, 34(6), 272-280. [65] He, H., Yu, X., Zhang, J., Song, S., & Letaief, K. B. (2021). Cell-free massive MIMO for 6G wireless communication networks. Journal of Communications and Information Networks, 6(4), 321-335. [66] Rummery, G. A., & Niranjan, M. (1994). On-line Q-learning using connectionist systems (Vol. 37, p. 14). Cambridge, UK: University of Cambridge, Department of Engineering. [67] Ali, S., Saad, W., Rajatheva, N., Chang, K., Steinbach, D., Sliwa, B., ... & Malik, H. (2020). 6G white paper on machine learning in wireless communication networks. arXiv preprint arXiv:2004.13875. [68] Pan, X., You, Y., Wang, Z., & Lu, C. (2017). Virtual to real reinforcement learning for autonomous driving. arXiv preprint arXiv:1704.03952.

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