MACHINE LEARNING MODEL TO PREDICT SNR IN 6G IOT NETWORKS
Keywords:
KNN, SVM, XGBoost, 6GAbstract
The emergence of sixth-generation (6G) wireless networks is expected to reshape communication by delivering ultra-reliable, low-latency, and high-capacity connections for large-scale Internet of Everything (IoE) scenarios. Yet, sustaining a strong signal-to-noise ratio (SNR) in terahertz (THz) and visible light communication (VLC) bands remains difficult due to severe propagation loss and environmental variability. This work applies machine learning classification methods—including Logistic Regression, Random Forest, and Multilayer Perceptron (MLP)—to the “6G IoT Intelligent Management Dataset” to examine beamforming optimization across varied conditions. Using feature engineering, SHAP-based interpretability, and empirical testing, the results reveal that beamforming gain and SNR improvement are key indicators of communication quality. The MLP delivers the best performance with an F1 score of 0.92, underscoring the promise of deep learning in adaptive transmission control. These outcomes highlight the role of context-aware, AI-enabled methods in addressing 6G reliability issues and supporting sustainable IoT connectivity.References
[1] Jiang W, Han B, Habibi M A, et al. The Road Towards 6G: A Comprehensive Survey. IEEE Open Journal of the Communications Society, 2021, 2: 334-366. DOI: 10.1109/OJCOMS.2021.3057679.
[2] Tataria H, Shafi M, Molisch A F, et al. 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities. Proceedings of the IEEE, 2021, 109(7): 1166-1199. DOI: 10.1109/JPROC.2021.3061701.
[3] Chowdhury M Z, Shahjalal M, Ahmed S, et al. 6G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directions. IEEE Open Journal of the Communications Society, 2020, 1: 957-975. DOI: 10.1109/OJCOMS.2020.3010270.
[4] Quy V K, Chehri A, Quy N M, et al. Innovative Trends in the 6G Era: A Comprehensive Survey of Architecture, Applications, Technologies, and Challenges. IEEE Access, 2023, 11: 39824-39844. DOI: 10.1109/ACCESS.2023.3269297.
[5] Hosmer D W, Lemeshow S, Sturdivant R X. Applied Logistic Regression. 3rd ed. Hoboken: Wiley, 2013.
[6] Breiman L. Random forests. Machine Learning, 2001, 45(1): 5-32.
[7] Cortes C, Vapnik V. Support-vector networks. Machine Learning, 1995, 20(3): 273-297.
[8] Chen T, Guestrin C. XGBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, 2016: 785-794.
[9] Cover T, Hart P. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
[10] Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Information Processing and Management, 2009, 45(4): 427-437.
[11] Al-Fuqaha A I, Guizani M, Al-Dweik A, et al. Toward 6G Communications: Vision and Key Technologies. IEEE Vehicular Technology Magazine, 2021, 16(4): 28-36.
[12] Tran T X, Hajisami A, Pandey P, et al. Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges. IEEE Communications Magazine, 2017, 55(4): 54-61.
[13] Elsayed M, Erol-Kantarci M. AI-Enabled Future Wireless Networks: A Comprehensive Survey. IEEE Access, 2021, 9: 29544-29573.
[14] Zhang X, Zhang H, Leung V C M. AI Empowered Intelligent Reflecting Surface Aided mmWave/THz Communications. IEEE Transactions on Communications, 2022, 70(2): 1374-1389.
[15] ziya07. 6G IoT Intelligent Management Dataset. Kaggle, 2023. Available at: https://www.kaggle.com/datasets/ziya07/6g-iot-intelligent-management-dataset.
[16] Saleh R A, Yanikomeroglu H, Elkashlan M. Optimized Beamforming in 6G with Computer Vision and Explainable AI. IEEE Access, 2023, 11: 128902-128918.
[17] Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 2002, 16: 321-357.