[1]Moysen, J., & Giupponi, L. (2018). From 4G to 5G: Self-organized network management meets machine learning. Computer Communications, 129, 248-268. https://doi.org/10.1016/j.comcom.2018.07.015
[2]J. Rodriguez, Fundamentals of 5G Mobile Networks. John Wiley & Sons, 2015.
[3] Kim, H. (2022). Artificial intelligence for 6G (pp. 978-3030950408). Springer.https://doi.org/10.1002/9781118867464
[4] Akyildiz, I. F., Kak, A., & Nie, S. (2020). 6G and beyond: The future of wireless communications systems. IEEE access, 8, 133995-134030. https://doi.org/10.1109/access.2020.3010896
[5] Klaine, P. V., Imran, M. A., Onireti, O., & Souza, R. D. (2017). A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials, 19(4), 2392-2431. https://doi.org/10.1109/comst.2017.2727878
[6] Sridharan, S. (2020). Machine learning (ML) in a 5G standalone (SA) self organizing network (SON). arXiv preprint arXiv:2011.12288. https://doi.org/10.14445/22312803/ijctt-v68i11p105
[7] Fourati, H., Maaloul, R., Chaari, L., & Jmaiel, M. (2021). Comprehensive survey on self-organizing cellular network approaches applied to 5G networks. Computer Networks, 199, 108435.
[8] Papidas, A. G., & Polyzos, G. C. (2022). Self-organizing networks for 5g and beyond: A view from the top. Future Internet, 14(3), 95. https://doi.org/10.1016/j.comnet.2021.108435
[9]Luo, F. L. (Ed.). (2020). Machine learning for future wireless communications. https://doi.org/10.1002/9781119562306
[10] Charbuty, B., & Abdulazeez, A. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20-28. https://doi.org/10.38094/jastt20165
[11] Li, W. H., & Qi, Z. (2018). Network selection algorithm based on decision tree in heterogeneous wireless networks. In MATEC Web of Conferences (Vol. 189, p. 04010). EDP Sciences. https://doi.org/10.1051/matecconf/201818904010
[12] Nethraa Sivakumar, Pooja Srinivasan, Nikhil Viswanath, and Venkateswaran N.( 2022) “Decision tree-based radio link failure prediction for 5G communication reliability,” ITU Journal on Future and Evolving Technologies, vol. 3, no. 2, pp. 142–156, Jul. 2022, doi: 10.52953/lzlj8762. https://doi.org/10.52953/lzlj8762
[13] Saeed, U., Jan, S. U., Lee, Y. D., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability engineering & system safety, 205, 107284. https://doi.org/10.1016/j.ress.2020.107284
[14] Fernandes, A. A. T., Figueiredo Filho, D. B., Rocha, E. C. D., & Nascimento, W. D. S. (2021). Read this paper if you want to learn logistic regression. Revista de Sociologia e Política, 28, 006. https://doi.org/10.1590/1678-987320287406en
[15] Kulkarni, A. M., Saini, G., & Pattnaik, S. S. (2023, March). Antenna array fault detection using logistic regression technique. In International Conference on Artificial Intelligence of Things (pp. 13-29). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-48781-1_2
[16] Sharma, V., Hong, T., Cecchi, V., Hofmann, A., & Lee, J. Y. (2023). Forecasting weather‐related power outages using weighted logistic regression. IET Smart Grid, 6(5), 470-479. https://doi.org/10.1049/stg2.12109
[17] Liu, Y., Wang, Y., & Zhang, J. (2012). New machine learning algorithm: Random forest. In Information Computing and Applications: Third International Conference, ICICA 2012, Chengde, China, September 14-16, 2012. Proceedings 3 (pp. 246-252). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34038-3
[18] Abd El-Aziz, L., Amr, E., Yehia, H., Mostfa, H., Hisham, M., Shenawy, A., ... & El-Akel, H. (2020, October). Cell outage detection and degradation classification based on alarms and kpi’s correlation. In 2020 2nd Novel intelligent and leading emerging sciences conference (NILES) (pp. 230-235). IEEE. https://doi.org/10.1109/niles50944.2020.9257920
[19] Samidi, F. S., Mohamed Radzi, N. A., Mohd Azmi, K. H., Mohd Aripin, N., & Azhar, N. A. (2022). 5G technology: ML hyperparameter tuning analysis for subcarrier spacing prediction model. Applied Sciences, 12(16), 8271. https://doi.org/10.3390/app12168271
[20] Ali‐Tolppa, J., Kajo, M., Gajic, B., Malanchini, I., Schultz, B., & Liao, Q. (2020). Cognitive Autonomy for Network Self‐Healing. Towards Cognitive Autonomous Networks: Network Management Automation for 5G and Beyond, 345-384. https://doi.org/10.1002/9781119586449.ch9
[21] Moysen, J. (2016). A cell outage management framework for dense heterogeneous networks. IEEE transactions on vehicular technology, 2016, vol. 65, núm. 4, p. 2097-2113. https://doi.org/10.1109/tvt.2015.2431371
[22]Zoha, A., Saeed, A., Imran, A., Imran, M. A., & Abu‐Dayya, A. (2016). A learning‐based approach for autonomous outage detection and coverage optimization. Transactions on Emerging Telecommunications Technologies, 27(3), 439-450. https://doi.org/10.1002/ett.2971
[23] Asghar, M. Z., Ahmed, F., & Hämäläinen, J. (2021, December). Artificial intelligence enabled self-healing for mobile network automation. In 2021 IEEE Globecom Workshops (GC Wkshps) (pp. 1-6). IEEE. https://doi.org/10.1109/gcwkshps52748.2021.9681937
[24] Yu, P., Wang, H., & Chen, Z. (2022, June). Active Cell Outage Detection Algorithm for Broadband Services in 5G Cloud Radio Access Networks. In 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) (pp. 1-5). IEEE. https://doi.org/10.1109/bmsb55706.2022.9828778
[25] Yu, P., Yang, X., Zhou, F., Li, H., Feng, L., Li, W., & Qiu, X. (2020). Deep reinforcement learning aided cell outage compensation framework in 5G cloud radio access networks. Mobile Networks and Applications, 25(5), 1644-1654. https://doi.org/10.1007/s11036-020-01574-8
[26] Iwamoto, M., Suzuki, A., & Kobayashi, M. (2023, May). Deep Reinforcement Learning Based Antenna Selection for Cell Outage Compensation. In ICC 2023-IEEE International Conference on Communications (pp. 3945-3950). IEEE. https://doi.org/10.1109/icc45041.2023.10279017