References
[1] G. Pocovi, H. Shariatmadari, G. Berardinelli, K. Pedersen, J. Steiner, and Z. Li, "Achieving ultra-reliable low-latency communications: Challenges and envisioned system enhancements," IEEE Network, vol. 32, pp. 8–15, [Online]. Available: https://doi.org/10.1109/MNET.2018.1700332 [2] C. She, C. Sun, Z. Gu, Y. Li, C. Yang, H. V. Poor, and B. Vucetic, "A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning," Proceedings of the IEEE, vol. 109, pp. 204–246, [Online]. Available: https://doi.org/10.1109/JPROC.2020.3048528 [3] Z. Shi, X. Xie, S. Garg, H. Lu, H. Yang, and Z. Xiong, "Deep reinforcement learning-based big data resource management for 5G/6G communications," in Proc. 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, Dec. 7–11, 2021, pp. 1–6. [Online]. Available: https://doi.org/10.1109/GLOBECOM46510.2021.9685312 [4] M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y. M. 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. [Online]. Available: https://doi.org/10.1109/OJCOMS.2020.3010270 [5] A. Salh, L. Audah, N. S. M. Shah, A. Alhammadi, Q. Abdullah, Y. H. Kim, S. A. Al-Gailani, S. A. Hamzah, and B. A. F. Esmail, "A survey on deep learning for ultra-reliable and low-latency communications challenges on 6G wireless systems," IEEE Access, vol. 9, pp. 55098–55131, [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3071562 [6] L. Meng, (2022). Analysis of the role of 5G communication technology based on intelligent sensor network in the construction and design of the Internet of Things in free trade zones. Journal of Sensors. https://doi.org/10.1155/2022/4418231 [7] S. Niknam, H. S. Dhillon, and J. H. Reed, "Federated learning for wireless communications: Motivation, opportunities, and challenges," IEEE Communications Magazine, vol. 58, pp. 46–51, [Online]. Available: https://doi.org/10.1109/MCOM.001.2000011 [8] K. H Bamniya, & R. K. Panchal (2024). A comprehensive review of 5G wireless communication: Spectrum diversity with technological innovations and current challenges. International Journal of Intelligent Systems and Applications in Engineering, 12(22s). [9] D. Caldarola, B. Caputo, M. Ciccone, and P. di Torino, "Improving generalization in federated learning by seeking flat minima," arXiv, [Online]. Available: https://arxiv.org/abs/2203.11834 [10] A. A. Al-Saedi, V. Boeva, and E. Casalicchio, "Reducing communication overhead of federated learning through clustering analysis," in Proc. 2021 IEEE Symposium on Computers and Communications (ISCC), Athens, Greece, Sep. 5–8, 2021, pp. 1–7. [Online]. Available: https://doi.org/10.1109/ISCC53001.2021.9631472 [11] Mendonça, S., Damásio, B., Charlita de Freitas, L., Oliveira, L., Cichy, M., & Nicita, A. (2022). The rise of 5G technologies and systems: A quantitative analysis of knowledge production. Telecommunications Policy, 46(4), Article 102327. https://doi.org/10.1016/j.telpol.2022.102327 [12] P. M. Mammen, "Federated learning: Opportunities and challenges," arXiv, 2021. [Online]. Available: https://arxiv.org/abs/2101.05428 [13] Patel, B., Yarlagadda, V. K., Dhameliya, N., & Mullangi, K. (2022). Advancements in 5G technology: Enhancing connectivity and performance in communication engineering. Engineering International, 10(2), 117–130. https://doi.org/10.18034/ei.v10i2.715 [14] J. Kone?ný, H. B. McMahan, F. X. Yu, P. Richtárik, A. T. Suresh, and D. Bacon, "Federated learning: Strategies for improving communication efficiency," arXiv, 2016. [Online]. Available: https://arxiv.org/abs/1610.05492 [15] N. H. Tran, W. Bao, A. Zomaya, M. N. H. Nguyen, and C. S. Hong, "Federated learning over wireless networks: Optimization model design and analysis," in Proc. IEEE Conference on Computer Communications, Paris, France, Apr. 29–May 2, 2019, pp. 1387–1395. [Online]. Available: https://doi.org/10.1109/INFOCOM.2019.8737419 [16] Y. Liu, X. Yuan, Z. Xiong, J. Kang, X. Wang, and D. Niyato, "Federated learning for 6G communications: Challenges, methods, and future directions," China Communications, vol. 17, pp. 105–118, 2020. [Online]. Available: https://doi.org/10.23919/JCC.2020.09.008 [17] K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Kone?ný, S. Mazzocchi, B. McMahan, et al., "Towards federated learning at scale: System design," SysML, 2019. [Online]. Available: https://arxiv.org/abs/1902.01046 [18] S. Kamath, S. Anand, S. Buchke, & K. Agnihotri. (2024). A review of recent developments in 6G communications systems. Engineering Proceedings, 59(1), Article 167. Presented at the International Conference on Recent Advances in Science and Engineering, Dubai, United Arab Emirates, 4–5 October 2023. https://doi.org/10.3390/engproc2023059167 [19] S. Sengar, & P. K. Malik. (2022). A comprehensive survey of massive-MIMO based on 5G antennas. International Journal of RF and Microwave Computer-Aided Engineering, 32(4), Article e23496. https://doi.org/10.1002/mmce.23496
