DOI: 10.11648/j.wcmc.20261301.11 ISSN: 2330-1015

Comparative Analysis of Random Forest and Bayesian Support Vector Machine for Signal-to-Noise Prediction in Satellite-Terrestrial Networks

Oladayo Ajileye, Vincent Akpan, Joseph Ojo
Show PDF Cite
The convergence of terrestrial wireless and satellite communication systems within Satellite–Terrestrial Integrated Networks (STINs) for sixth-generation (6G) communications has increased the need for accurate and adaptive link performance prediction. Conventional analytical and empirical propagation models often fail to capture the nonlinear effects of atmospheric impairments, particularly rain attenuation in tropical regions. This study evaluates two supervised machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), for predicting the signal-to-noise ratio (SNR), a key wireless communication link budget parameter. Rain rate, rain attenuation, and link budget were used as input variables with propagation datasets collected from ten climatically diverse locations across Nigeria. The datasets were partitioned into training, validation, and testing sets using a 70:15:15 ratio, while Bayesian optimization determined the optimal hyper-parameters of both models. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), coefficient of determination (R 2 ), regression analysis, and actual-versus-predicted time-series comparisons. Results show that both models accurately captured the nonlinear relationship between propagation parameters and SNR across all study locations. However, SVM consistently outperformed RF, recording lower RMSE, MAE, and MSE values in nine of the ten locations and achieving R 2 values between 0.99 and 1.00. SVM also tracked severe fading events caused by intense rain attenuation more accurately, whereas RF exhibited larger deviations during deep fades. Minna was the only location where RF produced lower prediction errors. These findings demonstrate that SVM provides superior prediction accuracy and generalization for SNR estimation under tropical propagation conditions, making it a reliable tool for wireless link budget prediction and future 6G satellite–terrestrial network planning.

More from our Archive