Beyond-5G(B5G)/6G networks are expected to serve many applications (e.g., autonomous vehicles, remote surgery and digital twins) that pose stringent requirements on Telecommunications Network in terms of latency, data rates, reliability and availability. Thanks to the Network Slicing mechanism enabled by the network function virtualization (NFV) and Software Defined Networking (SDN) techniques, the B5G/6G networks exhibit immense potential for meeting these challenges posed by the stringent application requirements. In the last decade, there has been a significant progress in the area of network slicing for core networks and many new approaches/tools have been proposed. However, the emerging dominance of Mobile Edge computing (MEC) and its strong coupling with Radio access networks (RANs) require a complete rethinking of the Network Slicing mechanism considering wireless access. In particular, the integration of RAN, core networks and MEC brings new challenges for enabling dynamic end-to-end network resource allocation. The main objective of this PhD position is to explore novel methods/techniques to meet the end-to-end network slicing requirements considering both the core networking and wireless access. In particular, the application of novel machine learning techniques such as Deep reinforcement learning (DRL) will be explored by the PhD candidate.
As part of the Advanced Networking Lab and the Center for Wireless Technology (CWTe), the project invites candidates with strong analytical skills and willingness to test novel approaches on our 5G Core, Backhaul and Radio Access Network laboratory testbeds.
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