Master Thesis Opportunity– Machine Learning for Coordinated Beamforming in 5G Networks

Location: Kista, Stockholm

Preferred starting date: Jan. 2026

Extent: 1-2 student, 30hp.

About The Company

Founded in 1987, Huawei Technologies is one of the fastest growing telecommunications and network solutions providers in the world. At Huawei Technologies, we look for people who share our vision: to enrich life with communication. We are a leading supplier of next generation telecom networks and currently serve 37 of the world’s top 50 operators. Our people are committed to providing innovative products, services and solutions and understand it as their mission to create long-term value and growth potential for our clients.

The Huawei office in Sweden is the leading overseas R&D office in Huawei, and the Wireless Algorithm group at Huawei Sweden drives innovation for the Huawei Wireless RAN product. We work on both advanced receivers and on Radio Resource Management algorithms, for both LTE and 5G.

Thesis description:
Beamforming is a linear spatial processing technique used at basestations (BS) to communicate to multiple users avoiding interference. However, in practical networks with multiple BS, beamforming weights should be coordinated, otherwise the interference leakage to users served by other BS will degrade the network performance. Coordination typically requires that each BS knows the active users served by other BS, but in practice this information is delayed (outdated), making coordinated beamforming (CBF) challenging.

Project goal:
Use distributed machine learning to predict coordinated beamforming weights locally at each BS, relying on locally observed channels and historically exchanged (possibly delayed) neighbor information, to provide a better beamforming coordination and in return better network performance.

Tasks:

1. Build a Python simulator that models a practical distributed RAN with realistic traffic dynamics
2. Develop ML methods that run at each cluster and output beamforming weight matrices using local channel observations and historical neighbor info example GNNs.
3. Evaluate and compare ML solutions against traditional algorithms

Your Profile

• Master student in Electrical Engineering, computer science or equivalent.
• A solid theoretical background in areas such as mathematics, information theory and signal processing. Knowledge of linear algebra, probability, and optimization.
• Experience in machine learning and AI, familiar with deep learning models.
• Good knowledge in simulators, proficiency in Python and PyTorch.

For more information regarding this opportunity, please contact:

Ahmet Gokceoglu,  Ahmet.Hasim.Gokceoglu1@Huawei.Com