Master Thesis: Characterizing hardware imperfection of power amplifiers

Teknik, data och digitalt · Data, AI och analys · Maskininlärning · Datavetenskap · Mjukvaruutveckling

I korthet

This Master Thesis project at Ericsson in Stockholm focuses on modeling hardware imperfections in power amplifiers using advanced techniques like neural networks. You will work with real testbed data to investigate and develop models, comparing their performance against existing methods to enhance radio system reliability.

Ansvarsområden

Investigate state-of-the-art research on conventional and neural network-based models for hardware imperfections.
Suggest suitable models for the imperfection under study.
Implement models in the existing simulation framework.
Compare model performance and complexity with existing ones.
Perform analytical studies on model performance to gain insights into imperfections.

Krav

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

Förmåner

Outstanding opportunity to use skills and imagination to push boundaries.
Chance to build solutions never seen before.
Challenging environment with a team of diverse innovators.
Opportunity to craft what comes next.
Encouraging a diverse and inclusive organization.
Championing diversity in everything we do.
Collaboration with people with different experiences drives innovation.
Ericsson is proud to be an Equal Opportunity Employer.

Färdigheter

deep learningconvolutional neural networksLong-short term memorytransformerradio frequencysignal processingArtificial IntelligenceAnalysisC++AlgorithmsAgileAcademic WritingAPIArduino Software5GArchitecture

#hardware#power amplifiers#neural networks#thesis

Om Ericsson

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