Wireless Communication Channel Scenarios: Machine-Learning-Based Identification and Performance Enhancement

Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for sc...

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Bibliographic Details
Published in:Electronics (Basel) 2022-10, Vol.11 (19), p.3253
Main Authors: Zaki, Amira, Métwalli, Ahmed, Aly, Moustafa H., Badawi, Waleed K.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Communications systems
Computational efficiency
Computing time
Datasets
Efficiency
Electronic data processing
K-nearest neighbors algorithm
Machine learning
Methods
Mobile communication systems
Neural networks
Performance enhancement
Preprocessing
Probabilistic models
Regularization
Rural areas
Satellites
Support vector machines
Wireless communication systems
Wireless communications
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Summary:Wireless communication channel scenario classification is crucial for new modern wireless technologies. Reducing the time consumed by the data preprocessing phase for such identification is also essential, especially for multiple-scenario transitions in 6G. Machine learning (ML) has been used for scenario identification tasks. In this paper, the least absolute shrinkage and selection operator (LASSO) is used instead of ElasticNet in order to reduce the computational time of data preprocessing for ML. Moreover, the computational time and performance of different ML models are evaluated based on a regularization technique. The obtained results reveal that the LASSO operator achieves the same feature selection performance as ElasticNet; however, the LASSO operator consumes less computational time. The achieved run time of LASSO is 0.33 s, while the ElasticNet corresponding value is 0.67 s. The identification for each specific class for K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and k-Means and Gaussian Mixture Model (GMM) is evaluated using Receiver Operating Characteristics (ROC) curves and Area Under the Curve (AUC) scores. The KNN algorithm has the highest class-average AUC score at 0.998, compared to SVM, k-Means, and GMM with values of 0.994, 0.983, and 0.989, respectively. The GMM is the fastest algorithm among others, having the lowest classification time at 0.087 s, compared to SVM, k-Means, and GMM with values of 0.155, 0.26, and 0.087, respectively.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11193253