Master Thesis: Antenna System and Technology

Accurate antenna beam shaping is increasingly important as wireless networks demand greater capacity and reliability. This thesis at Ericsson will investigate how machine learning can identify excitation vectors that improve beam patterns while accounting for practical array effects, including mutual coupling. The goal is to develop a model that improves beam-weight design and delivers measurable antenna-system performance gains.

The assignment

• Study how beam weights affect antenna performance, including the suppression of side lobes.
• Explore relationships and dependencies between cost functions and excitation vectors for different antenna geometries.
• Build an optimized model for excitation vectors, combining antenna theory, electromagnetic modeling, optimization and machine learning.
• Evaluate the model with established metrics from machine learning and antenna design.
• Validate the results against measurement data.

Who we are looking for

• Currently studying for a master's degree in Engineering Physics, Electrical Engineering, Computer Engineering or a related discipline.
• Knowledge of electromagnetic field theory.
• Programming experience with Python and MATLAB.
• Experience applying machine learning techniques.
• A systematic, analytical approach to solving problems.

What we offer

• An opportunity to apply technical skills and creative thinking to challenging wireless technology problems.
• Collaboration with a team of innovators working on new technical solutions.

Practical details

Location: Göteborg. The project is intended for two master's students. Job reference: 790796.