5G and Beyond: Channel Classification Enhancement Using VIF-Driven Preprocessing and Machine Learning

The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario ide...

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Bibliographic Details
Published in:Electronics (Basel) 2023-08, Vol.12 (16), p.3496
Main Authors: Zaki, Amira, Métwalli, Ahmed, Aly, Moustafa H., Badawi, Waleed K.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automatic classification
Classification
Datasets
Decision making
Decision trees
Feature selection
Internet of Things
Machine learning
Methods
Mobile communication systems
Preprocessing
Probabilistic models
Quality of service
Regularization
Rural areas
Satellites
Signal processing
Support vector machines
Variance
Wireless communication systems
Wireless communications
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Summary:The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that adding the variance inflation factor (VIF) elimination layer has a significant effect in eliminating the residual noise after regularization. By evaluating the VIF, the high multi-collinear features are removed after adding a regularization penalty. Consequently, the total explained variance (TEV) was enhanced by 5% and reached 76%. Thus, the classification accuracy of the identification processes of different rural and urban scenarios was increased by 3%, on average, compared with previous work for each algorithm: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Gaussian Mixture model (GMM).
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12163496