Enhancing Radio Access Network Slicing via Machine Learning Predictions

Networks are currently required to dynamically adapt to meet the distinct requirements of diverse traffic classes. Emerging classes of traffic such as those generated by autonomous vehicles and various machine-to-machine communications are adding another dimension to the already congested wireless channels [1]. To meet the requirement of this class diversity and high demand, slicing the radio access network (RAN), which complements core and transport slicing, has gained significant popularity, both from academia and industry [2]. In fact, there are now a number of network equipment manufacturers offering different forms of slicing capabilities, but we are yet to see the full potential of this exciting concept. And despite these available options in the market, end-to-end network slicing remain in the development phase and largely still under investigation. However, it is expected to dominate future network access mechanisms, both in the core and the edge of the network.

More information on the project, from potential impact to references, can be found on the accompanying PDF.

To apply, please complete the project proposal form and the online application.

Only local Hubs members can access this page. Join the community today: https://archive.phdhub.eu/register