Master Thesis: Characterizing hardware imperfection of power amplifiers

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About this opportunity:



In the field of electronics, hardware components exhibit a wide range of behaviors that can vary significantly under different conditions impacting their performance or operation accuracy and efficiency. These deviations are commonly known as hardware imperfections. Modeling the behavior of these devices is often a complex and challenging task. Our hardware engineering unit at Ericsson holds a keen interest in modeling and predicting the behavior of such devices.

At the forefront of this endeavor, state-of-the-art techniques such as neural networks have gained recognition for their ability to accurately capture and predict the diverse behaviors exhibited by such devices. By leveraging the power of neural networks, engineers and researchers are able to develop models that effectively characterize often unpredictable nature of these devices.

Accurate and reliable models can bring insight to the ways that we could mitigate these behaviors or avoid the circumstances where these behaviours happens paving the way for more advanced and reliable radio systems.

What you will do:



During this thesis work you have the opportunity to work with realistic data coming from our testbeds to study the actual hardware imperfections. The thesis work will be organized around one or two type of hardware imperfection. The end goal of the work is to develop one or more models and compare the performance of these models with the existing ones toward the following steps:

• Investigate the state of the art research covering different types of models including conventional and neural network based models.
• Suggest models suitable for the imperfection under study, implement it in our existing simulation framework and compare the performance and the complexity of the model with existing ones.
• Perform a short analytical study on the performance of the model providing insights to the imperfection.

The skills you bring:

• This project aims at students in electrical engineering, computer science, machine learning or similar.
• Background or experience with signal processing, deep learning (e.g., convolutional neural networks, Long-short term memory, transformer based) and radio frequency components is preferred.

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: 791653