5G Network Slice Type Classification using Traditional and Incremental Learning
Mohamad Ahmadinejad, Tahmina Azmin, Nashid Shahriar · 2023
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%.