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 assisted mobile networks are becoming a reality. Ongoing research intends to answer questions of how and what kind of machine intelligence can be incorporated into different network use cases. One such question is finding a suitable classification method for solving a certain machine learning use case.
The thesis work is proposed to cover:
- Investigate and evaluate the possibility to use multi-label classification.
- Realize multi-label classification using LightGBM
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, firstname.lastname@example.org
Last day of application is 10th of November 2019.
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Primary country and city: Sweden (SE) || || Linköping || R&D
Req ID: 302939