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