Аннотация
The advent of 5G technology promises to revolutionize wireless communication with unprecedented data speeds, ultra-low latency, and reliable connectivity. However, ensuring consistent Quality of Service (QoS) across diverse environments presents significant challenges. This study evaluates the effectiveness of various regression algorithms in predicting 5G QoS. By applying Multiple Linear Regression (MLR), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) to a comprehensive 5G dataset, we compare their performance based on key metrics. Our findings highlight SVM's superior capability in handling non-linear relationships, offering a robust solution for 5G QoS prediction.