- Master student in Electrical, information technology Engineering or equivalent.
- Knowledge in machine learning, Python coding.
- Knowledge in cellular communication (knowledge in 3GPP systems and standards is appreciated).
- Autonomy, innovative
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Master Thesis: ML-based UL Power Control - Lund, Sverige - Huawei Sweden R&D
Beskrivning
Location: Ideon, Lund
Preferred starting date: January 2024
For whom: 1-2 students
Period: 6 months
Thesis description
Artificial intelligence, and more precisely machine learning, have proven their worth in a multitude of fields, including classification, regression, and interaction with dynamic environments (i.e. reinforcement learning). Given the ability of some machine learning models to understand patterns and dependencies in the data, exploiting their potential in the air interface of wireless communication networks could deliver non-negligible performance gains.
The 3GPP standardization is working on AI/ML for the 5G NR air interface. One of the critical goals for 6G is to achieve key performance improvements with minimum energy consumption. In this context, one of the main components that can be enhanced is uplink (UL) power control. UL power control is critical in managing inter-cell and intra-cell interference as well as the power consumption in the phone.
The objective of this project is to study different ML approaches, mainly reinforcement learning, for UL transmit power control in connected state. The proposed scheme should enable power consumption reduction in the phone, in a cell-free/distributed MIMO or multi-TRP setting. Additionally, the impact of hardware distortions due to different transmit power levels can be further considered.
Qualifications
Interested? Reach out to:
Vishnu Preman
