Radio scheduling for eXtended Reality (XR) in 5G Networks (with Ericsson Research)


Extended Reality (XR), which includes Cloud Gaming (CG) and Augmented/Virtual/Mixed Reality (AR/VR/MR) applications, is a key use case for 5G and beyond-5G systems. In order to satisfy XR requirements in terms of throughput and latency, several challenges are yet to be   addressed.   In   particular,   XR   applications   require   heavy   and   nearly-periodic   data transmission in both downlink and uplink, thus calling for significant enhancements in the scheduling and resource allocation mechanisms currently used at the radio access level. 

Learning outcome

This thesis will explore the possibility of proposing and validating enhanced mechanisms for scheduling and resource allocation in 5G and beyond-5G radio access networks, in order to support   XR   services   and   meet   quality   of   service   and   experience   requirements.   The investigation will leverage existing mechanisms for scheduling and resource allocation in 5G New Radio, such as dynamic scheduling, semi-persistent scheduling,  and configured grant, aiming at enhancing and tailoring such mechanisms for XR traffic. The use of machine learning and artificial intelligence schemes will also be considered, in order to further optimize the proposed schemes.      

Learning outcome

Good understanding of mobile communication systems (4G, LTE, 5G NR), practical experience with network simulators, ML-oriented Analytics.


Knowledge of programming languages (Matlab, Python), preferred knowledge of network simulators (e.g., ns-3, OMNeT++),  preferred  knowledge  of  Machine  and  Deep  Learning  schemes  (e.g.,  Reinforcement Learning) 


Emneord: 5G networks, Extended Reality (XR), Machine Learning, Network, data analysis
Publisert 27. sep. 2022 09:36 - Sist endret 27. sep. 2022 09:36

Omfang (studiepoeng)