Master thesis Hybrid-AI Algorithm for 5G Channel Prediction

Location: Kista, Stockholm

Preferred starting date: Jan. 2025

Extent: 1 student, 30hp.

About the company

Founded in 1988, 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

This Master’s thesis offers the chance to explore cutting-edge AI techniques to tackle one of the biggest challenges in modern wireless networks: the time-varying nature of communication channels. In this project, you will develop and apply hybrid AI models—blending classical channel prediction methods and deep learning—to predict and adapt to dynamic changes in the communication environment. With mobile networks rapidly evolving, from 5G to beyond, this research will put you at the forefront of innovations that enhance the adaptability and precision of channel prediction, leading to more efficient signal processing and better resource management in modern telecommunication networks. If you’re ready to bridge the gap between AI and telecom, this thesis is for you!

Qualifications

• Master student in Electrical Engineering or equivalent.


• A solid theoretical background in areas such as signal processing and machine learning.


• Experience in modeling and link level simulation.

• Good knowledge in simulators.

Contact person

Leo Wang

[email protected]