In the ever-evolving cellular networks, more and more frequencies are being deployed in the 4G Long Term Evolution (LTE) and the 5G New Radio (NR) networks all over the world. The deployed frequencies need to be efficiently managed to provide better (video streaming) clients experiences. Load balancing serves the purpose of providing higher end user Quality of Service (QoS). It also enables managing network resources with minimal operational and maintenance cost. Traffic load balancing is achieved by relocating mobiles to frequencies that are under-utilized in comparison with the frequency in use. However, one needs to avoid overloading a target frequency by unnecessarily relocating too many mobiles without knowing the load situation after the relocation. To counter these, predicting traffic load via Machine Learning (ML) algorithms on the target frequency will allow us to achieve an even distribution of traffic load between different frequencies. Therefore, we want to investigate which ML algorithm best fits the proposed idea and gives the best possible result in terms of prediction accuracy as well as optimizing network resources.
This thesis is divided into several steps with the end goal of showing a prototype of how ML based traffic load prediction can be useful in efficiently utilizing different frequencies, thus, optimizing a 4G/5G network.
The following steps are envisioned as part of the thesis work:
- Investigate and compare applying different types of ML techniques to load balancing
- Demonstrate feasibility of the proposed approach with at least two or three ML techniques.
- Analyze results of the investigation and evaluate the expected gain.
This project aims at students in electrical engineering, computer science, computer engineering or similar.
Valuable skills are:
- Good knowledge in Machine Learning
- Good programming skills in MATLAB, Python or R
- Understanding of telecommunications, wireless communications and cellular networks is preferred
- Good communication skills in English
1-2 students, 30hp each
Ericsson AB Mjärdevi, Linköping
Last Day to Apply:
16th of November
Preferred Starting Date:
Hasibur Rahman, email@example.com
Ove Linnell, firstname.lastname@example.org
Agnes Westerberg, email@example.com
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Primary country and city: Sweden (SE) || || Linköping || Stud&YP
Req ID: 302838