5G Network Slice Type Classification using Traditional and Incremental Learning
Authors:
- 8 May 2023
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Abstract
The Fifth generation (5G) mobile network is expected to provide high bandwidth, low latency, and rapid user connectivity. 5G Mobile operators are seeking an effective solution that would enable them to support heterogeneous use cases with different Quality of Service (QoS) requirements by utilizing the existing physical infrastructure. 5G supports Network Slicing (NS), an end-to-end (E2E) logical network that is mutually isolated, has independent control, and can be managed independently. By slicing the network, mobile operators can effectively manage several network instances over a single infrastructure to provide a variety of applications, use cases, and business services while satisfying heterogeneous QoS requirements. With the advancement of Machine Learning (ML), future communication networks will need to use data-driven decision-making to achieve desired network performance. In this paper, we demonstrated a prediction mechanism using Machine and Deep Learning Algorithms in traditional and incremental ways to select the suitable network slice for various user requirements and device types. Using a publicly available dataset and Incremental Learning model called Stochastic Gradient Descent (SGD), we successfully classified incoming user requests to appropriate network slices with an accuracy of 99.33%.Keywords
This publication has 19 references indexed in Scilit:
- Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning ApproachJournal of Network and Systems Management, 2022
- Optimal 5G network slicing using machine learning and deep learning conceptsComputer Standards & Interfaces, 2021
- Machine Learning-based Slice allocation Algorithms in 5G Networks2019
- Deep learning convolutional neural network (CNN) With Gaussian mixture model for predicting pancreatic cancerMultimedia Tools and Applications, 2019
- An SDN/NFV based framework for management and deployment of service based 5G core networkChina Communications, 2018
- Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset sizeCATENA, 2016
- Identifying Feature Relevance Using a Random Forest2006
- Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7.The Knowledge Engineering Review, 1998
- The context-tree weighting method: basic propertiesIEEE Transactions on Information Theory, 1995
- A survey of decision tree classifier methodologyIEEE Transactions on Systems, Man, and Cybernetics, 1991