Compensation of PA Distortion for 6G Communication Systems

Non-linearity of power amplifiers (PA) is a critical issue in communication system. The nonlinearity generates spectral re-growth, which leads to adjacent channel interference and violations of the out-of-band emissions standards mandated by regulatory bodies. It also causes in-band distortion, which degrades the bit-error rate (BER) and data throughput of the communication system. To avoid nonlinear distortions, the PA is intentionally backed off to a linear region with lower transmission power. However, this significantly degrades the power efficiency and the communications range. Alternatively, pre-compensating the distortion would allow operation in a more power-efficient region of the PA.  Digital pre-distortion (DPD) is a widely used technique to enhance linearity and compensate for non-linearity in power amplifiers. Meanwhile, advanced PA technologies, such as the Doherty PA and envelope tracking, aim to improve efficiency not only at peak output power but also in the back-off region, making them highly suitable for modern high-PAPR signals. Efficiently compensating for the non-linearity of these technologies across different frequency bands, including FR2 and FR3, remains an open and challenging area of research. Addressing these challenges is key to achieving both higher efficiency and better linearity in next-generation wireless communication systems. 

In this research, the student will learn the nonlinearity of PA and the state-of-the-art digital pre-distortion (DPD) algorithms. With the knowledge of DPD algorithms, the student will further explore DPD schemes for MIMO system in 6G or apply DPD algorithms for different PAs for lower frequency bands. During this thesis you will have the opportunity to collaborate closely with our supportive and experienced team and strengthen your skills for signal processing and wireless system simulation. 

The successful candidate will have the following skills: 

- knowledge of wireless communications and signal processing 

- proficiency with Matlab 

- knowledge of machine learning is a plus