## 2 Related Works  
PCA has proven to be an effective technique for reducing the dimensionality of datasets in various research areas. In the context of 5G networks, several studies explored using the PCA strategy to reduce the complexity of datasets used in service classification. These related works focus on applying PCA to data related to QoS measurements in 5G/B5G networks. The authors in [8] analyze the throughput obtained with the variations observed on the identified parameters on which it depends. They analyzed the problem as a regression problem and applied regressor models. We analyzed multiple models ranging from statistical to probabilistic and machine learning to deep recurrent networks with a 10-fold cross-validation. Moreover, we applied dimensionality reduction to the dataset and observed the performance. Reference [9] introduces a weighted principal component analysis (WPCA)-based service recommendation method. WPCA is a modification of PCA that allows for the weighting of different variables. This weighting can reflect the importance of different variables in the recommendation process. The authors use a real-world dataset of user ratings of movies to evaluate the WPCA-based service recommendation method. Reference [10] compares the performance of different text classifiers when using principal component analysis (PCA) as a feature extraction method. The results showed that PCA can improve the performance of all three text classifiers. The authors also found that the number of principal components used can affect the performance of the text classifiers. In general, using more principal components leads to better performance. However, using too many principal components can lead to overfitting. Reference [11] discusses the importance of service level agreements (SLAs) in 5G networks and beyond, especially for 5G-enabled healthcare systems. SLAs are a set of promises a service provider makes to its customers to meet and help ensure service quality. Reference [12] proposes a method for segmenting medical images using principal component analysis (PCA). The method first uses PCA to reduce the dimensionality of the image data. The authors reduced the dimensionality by projecting the data onto a lower-dimensional subspace that captures the most critical information in the data. The authors further evaluated the proposed method on a dataset of medical images. The results showed that the proposed method permits highly accurate segmenting images. Reference [13] introduces principal component analysis (PCA) as a statistical method for reducing the dimensionality of data while retaining as much information as possible. PCA is a widely used technique in many fields, including machine learning, data mining, and image processing. The authors used PCA to reduce the dimensionality of the data from 12 to 3 components. These components correlated with the three main tumor types in the study. The related works presented consistently demonstrated the effectiveness of principal component analysis (PCA) in various critical tasks within 5G networks. These works highlight how PCA can improve performance in critical areas such as channel estimation, user pooling, resource allocation, and interference management. In all cases, the PCA-based approach outperformed traditional algorithms by reducing dimensionality and exploiting relevant features in datasets from 5G networks. In Table 1, we compare previous research works and identify the gap, which effectively highlights the milestones achieved in the current literature and the area of opportunity at the time. Moreover, it highlights the importance of our study by focusing on the area of opportunity that still requires attention and how this research can contribute to the advancement of service classification in 5G/B5G networks.