Enhanced Mobile Broadband (eMBB) is one of three primary 5G New Radio (NR) use cases defined by the 3GPP. Within eMBB, 5G will need to deliver higher capacity, increased data rates, seamless coverage and higher user mobility. Whilst higher user mobility up to 500km/hour in high-speed trains and up to 1,000km/hour in airplanes, with seamless connectivity and enhanced user experience. Furthermore, in NR, 0ms interruption is one of the requirements to provide seamless handover user experience. Mobility performance is one of the most important performance metric for NR.Therefore, it is important to identify handover solutions to achieve high handover performance with 0ms interruption, low latency and high reliability.
Self-Organizing Networks (SON) was introduced in LTE to support automation of planning, configuration, management, optimization and healing of mobile radio access networks. Mobility Robustness Optimization (MRO) is one of the SON features which aims at detecting and enabling correction of unnecessary handovers, connection failures due to mobility, inter/intra cell ping-pong, which will deteriorate user experience and waste network resources. MRO achieves this by continuously optimizing HO configuration parameters.
With 5G NR the number of cells will drastically increase to provide more coverage and to enable seamless connectivity. To enhance user experience under higher mobility within these cells, MRO solution should also be extended in 5G NR to detect and resolve problems mentioned previously. Machine learning (ML) is a category of algorithms that allows software applications to become more accurate in predicting outcomes by statistical analysis of large amount of data.
This thesis work will investigate suitable Machine Learning (ML) techniques to fit in for MRO in 5G NR. In this context, the thesis will propose and evaluate ML approaches for MRO in simulated environments like Matlab and potentially within a testbed setup. The substantial thesis goals will span the relative performance and robustness of evaluated ML techniques compared to traditional techniques for MRO.
Thus, the thesis work will involve:
- Investigating prior art to identify existing MRO techniques and potential drawbacks with them
- Proposing a ML technique for MRO to fill up the lacuna
- Developing a simulation model to evaluate prior art and proposed ML technique and simulating various possible scenarios
- Writing a thorough thesis report on the work done.
- Possibly publish the research work in a journal or a conference.
This project aims at students in electrical engineering, computer science, computer engineering or similar. Background in wireless communication is preferred.
We are looking for students with knowledge/experience in:
- Machine Learning
- Wireless Information Processing
- Cellular Communication
Extent: 1-2 students
Location: Lund, Sweden
Preferred Start Date: 2020-01-15
Are you interested?
Then send in your application (CV, current grades and cover letter written in English collated into one document) as soon as possible.
The application deadline is the 8th of December. The process will be ongoing, and we will let you know as soon as we can if you move forward.
For questions, please contact Recruiter Emelie Nygård at email@example.com.
The positions are placed within Uplink Layer 1 domain in Baseband Lower Products sector of Product Development Unit Baseband Software. The baseband SW implements the layer 1 and layer 2 protocol layers of the Ericsson RAN product family. The SW is deployed on Ericsson baseband boards and on Ericsson radio boards where the baseband SW interacts with layer 3 SW, Radio modules and O&M.
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Primary country and city: Sweden (SE) || || Lund || R&D