Master Thesis: AI‑Enhanced Next‑Gen DPD for 1 GHz+ Instantaneous Bandwidth Power Amplifiers

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 About this opportunity: Modern radio access networks increasingly rely on wideband power amplifiers (PAs) with instantaneous bandwidths of 1 GHz and beyond. These PAs exhibit strong nonlinearities and memory effects, which can cause spectral regrowth, degraded error-vector magnitude (EVM), and reduced energy efficiency. Digital predistortion (DPD) is essential for linearizing PAs, but conventional approaches can become difficult to scale to very wide bandwidths. They may require complex models, extensive tuning, and significant computational and memory resources. Recent advances in AI and machine learning offer new opportunities for more efficient model structures, parameterization, and adaptation. This thesis will investigate next-generation DPD models for 1 GHz+ bandwidth PAs, with a focus on hybrid approaches that combine domain knowledge of PA behavior with AI/ML techniques. The work will primarily involve simulation and offline processing of realistic PA models and/or measured data from Ericsson-relevant scenarios. Laboratory validation may be included where appropriate.

Review state-of-the-art DPD methods for wideband power amplifiers, including polynomial, LUT-based, and AI/ML-based approaches. Design and implement one or more DPD model structures that efficiently capture wideband memory effects and frequency-dependent PA behavior. Explore AI/ML for applications such as feature learning, model selection, parameter initialization, or improved tuning—while maintaining practical and interpretable solutions. Develop an evaluation framework in MATLAB, Python, or a similar environment using realistic PA models and/or measured PA data. Evaluate and compare the proposed approach with a representative baseline in terms of linearization performance, robustness, stability, computational complexity, and memory requirements. Provide recommendations for future DPD development in Ericsson wideband radios, including potential follow-up work on real-time implementation, faster tuning, and live adaptation. The skills you bring: Pursuing Master’s in Electrical Engineering, Applied Physics, Computer Science, or a related field. Strong interest in signal processing, communication systems, RF technologies and power amplifiers. Programming skills in MATLAB and Python. Knowledge of digital predistortion, RF systems, nonlinear systems, or machine learning is an advantage. You are analytical, structured, and able to work independently on an algorithm-development problem. You are motivated by working at the intersection of advanced algorithms, AI, and next-generation radio technology.   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:  789188