5G Network Slicing: Analysis of Multiple Machine Learning Classifiers
This work provides a comparative analysis of ML methods for optimizing 5G network slicing, which is incremental as it applies existing techniques to a specific telecommunications problem.
This paper assessed multiple machine learning classifiers, including logistic regression, decision trees, and random forests, to detect 5G network slices, finding that random forest achieved the highest accuracy at 95% and precision at 94%.
The division of one physical 5G communications infrastructure into several virtual network slices with distinct characteristics such as bandwidth, latency, reliability, security, and service quality is known as 5G network slicing. Each slice is a separate logical network that meets the requirements of specific services or use cases, such as virtual reality, gaming, autonomous vehicles, or industrial automation. The network slice can be adjusted dynamically to meet the changing demands of the service, resulting in a more cost-effective and efficient approach to delivering diverse services and applications over a shared infrastructure. This paper assesses various machine learning techniques, including the logistic regression model, linear discriminant model, k-nearest neighbor's model, decision tree model, random forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the accuracy and precision of each model on detecting network slices. The report also gives an overview of 5G network slicing.