Mobile networks are used all over the world and are the corner stone for the networked society, where everything shall be connected. To support the vast amount and diversity of data expected in future networks, Ericsson are developing products to drive and support the networked society. The subjects for Master Thesis are defined to investigate and develop algorithms, architecture, tools etc. to support huge increase of speech, data and massive IoT for Radio Access Networks.
Machine Learning in Ericsson’s radio networks is a reality.
In order to configure and deploy complex radio networks, Machine Intelligence is used to predict radio coverage by measuring real mobile units.
A choice of good and various measurements will improve the Machine Intelligence models, and hence improve network performance.
The thesis work is proposed to cover:
Explore the impact of measurement values that enhance the estimate of the mobile unit geographical position, which would lead to a more accurate Machine Intelligence.
The thesis will be concluded with a result presentation for the Ericsson team.
This project aims at students in electrical engineering, computer science, computer engineering or similar.
1-2 students, 30hp each
Ericsson AB Mjärdevi, Linköping
Preferred Starting Date
For any questions or enquiries, please contact recruiter Sarah Lashari, email@example.com
Last day of application is 10th of November 2019.
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Primary country and city: Sweden (SE) || || Linköping || R&D
Req ID: 302933