Explainable AI framework for SNR prediction and adaptive beamforming in mmWave 5G networks

Abstract

Extremely fast development of the fifth-generation (5G) mobile networks, and in millimeter-wave (mmWave) issues, adaptive beamforming methods must be highly efficient to ensure stable signal-to-noise ratio (SNR) in various propagation conditions. The current paper describes an explainable artificial intelligence (XAI)-based system of SNR prediction that combines the domain knowledge of the antenna theory with machine learning models. A description database was designed with the implementation of domain-specific features, which included the essential parameters like array structure, propagation conditions, and side-lobe, and the use of RF chains. The wide scope of exploratory data analysis and statistical confirmation, such as ANOVA and ANCOVA, demonstrated that the behavior of SNR is very nonlinear and cannot be explained by point-specific factors. Then, LazyRegressor was used to compare the performance of multiple regression models, and feature engineering significantly improved the performance of the models by changing the values of the R2 to 1.00. Interpretability of the models was further improved with the help of SHAP analysis which allowed seeing the contribution of the parameters clearly. The suggested framework does not only go further to promote the accuracy of prediction but also forms an interpretable decision-supporting adaptive beamforming in mmWave 5 G systems. These results reveal the significance of domain knowledge and explainability in the development of next-generation wireless communication solutions.