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Master Thesis: Mobility Analysis Using Machine Learning

Job Description

Date: Oct 17, 2019

Ericsson Research

Ericsson Research develops new communication solutions and standards. The organization has responsibility to provide Ericsson with world-class system concepts, technology innovations, and methodologies. The rapidly increasing demands for mobile broadband access in combination with needs for new technology and solutions for the digitalization of industries and societies creates challenging and exciting opportunities for our organization. Building on our experience from real network operations, profound knowledge about existing standards, and forward looking research, we invent and master advanced concepts and solutions that shape today’s and tomorrow’s mobile radio communication.



Seamless wireless connectivity in different mobility scenarios is one of the fundamental requirements of fifth generation (5G) wireless networks targeting interruption-free mobility in high speeds (up to 500kmph). The quest to provide solutions meeting 5G networks requirements (i.e. seamless mobility) has already started. Performance of mobility protocols (e.g., mobility failure rate and interruption time) depends on the network radio conditions that is fluctuating in high mobility scenarios (e.g., highway, or high-speed trains). However, impairments caused by radio signal propagation can be mitigated by optimal configuration of mobility control parameters.

We want to investigate the impact of mobility control parameters on the performance of mobility protocols in LTE networks. We analyse the measurement data, collected in real field experiments, using machine learning techniques as well as classical statistical analysis. The aim is to identify the main components affecting the performance of LTE mobility protocols in different scenarios. The outcome of this study could potentially provide significant impact on the configuration of mobility control parameters in 5G networks.


Thesis Description

The following steps are envisioned as part of the thesis work:

  • Develop/extend parser to translate XML data into CSV file, ready to go for analysis
  • Analyse the data including
  • Main components analysis of handover interruption time
  • Impact of mobility control parameters on radio link failure
  • Performance of recovery procedure after radio link failure, e.g. re-establishment procedure

The thesis will be concluded with a result presentation for the Ericsson research team.

This project aims at students in telecommunication and networking engineering, computer science, computer engineering or similar.
Suitable for 1 or 2 students (30hp each).
All master program courses are preferably finished before start of thesis work.
Background in wireless communication is preferred.


Python, Mobility Protocols, Machine Learning, Self-Optimizing network


Ericsson AB Mjärdevi, Linköping


If you feel interested, please send in your application as soon as possible. The start date can be adjusted to both your and the business needs – the intention is to start in Spring 2020.
While applying please attach your updated CV, current grades and cover letter written in English into one document (under CV field in the application tool) and clearly define your technical knowledge.
For informal queries, feel free to email Samuel Axelsson (Hiring Manager) at or Ali Parichehreh (Supervisor) at or Elzbieta Penpeska (Recruiter)


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This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, training and development.

Ericsson expressly prohibits any form of workplace harassment based on race, color, religion, sex, sexual orientation, marital status, pregnancy, parental status, national origin, ethnic background, age, disability, political opinion, social status, veteran status, union membership or genetic information.


Primary country and city: Sweden (SE) || || Linköping || Stud&YP

Req ID: 303029