Master Thesis: Quantifying and predicting PIM from Carrie Configuration

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About this opportunity:

Passive Intermodulation (PIM) is an important source of unwanted interference in modern radio networks. As wireless networks evolve toward more complex multi-carrier configurations and increasingly crowded frequency bands, PIM can impact receiver performance and contribute to uplink interference.

Today, PIM-related issues are often investigated on a site-by-site basis. However, it can be challenging to distinguish PIM from other sources of uplink interference, such as external interference or radio-internal effects.

This thesis combines RF engineering, mathematical modeling and data analysis to develop a way of quantifying how different carrier configurations may increase the risk and impact of PIM.

You will develop and validate a mathematical model, apply it to real network-level data, and evaluate the results through both laboratory measurements and field data.

What you will do:

- Investigate how carrier frequencies, bandwidths, power levels and other configuration parameters may influence PIM risk.
- Analyze real network-level configuration and measurement data.
- Develop a mathematical, statistical or data-driven model to quantify PIM proneness and its potential impact on receiver performance
- Validate the model using laboratory measurements and/or real network measurements.
- Evaluate the accuracy, limitations and practical applicability of the proposed approach.
- Implement a prototype solution that can be applied to network-level datasets.
- Explore how the model could support proactive identification of potential PIM-related issues in operational networks.

The skills you bring:

- Master´s Degree in Electrical Engineering, Wireless Communications, Communication Systems, RF/Microwave Engineering, Electromagnetics, Signal Processing, or a related field.
- Good understanding of RF and wireless communication.
- Experience with or an interest in mathematical modelling and data analysis.
- Programming experience in Python, MATLAB or similar tools.
- A structured and scientific approach to investigating and validating technical problems.
- Good analytical and problem-solving skills.
- The ability to work independently while collaborating with engineers and other stakeholders.
- Experience with RF measurements, antennas, signal processing, statistical modelling or machine learning is a plus.

Why join Ericsson? At Ericsson, you´ll have an outstanding opportunity. The chance to use your skills and imagination to push the boundaries of what´s possible. To build solutions never seen before to some of the world’s toughest problems. You´ll be challenged, but you won’t be alone. You´ll be joining a team of diverse innovators, all driven to go beyond the status quo to craft what comes next.

What happens once you apply? Click Here to find all you need to know about what our typical hiring process looks like.Encouraging a diverse and inclusive organization is core to our values at Ericsson, that's why we champion it in everything we do. We truly believe that by collaborating with people with different experiences we drive innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity Employer.  learn more.

Primary country and city: Sweden (SE) || Stockholm

Req ID:  790978