Rohde & Schwarz proposed a project that would explore utilizing machine learning to facilitate the communications industry's move toward 6G. Digital Pre-Distortion (DPD) is a widely used technique to enhance the linearity of power amplifiers in communication systems. Our team developed a machine learning-based DPD algorithm that maintains signal quality (measured by Error Vector Magnitude (EVM)), is generalizable across various systems, and offers faster performance compared to iterative DPD algorithms by experimenting with different RNN architectures.

Team Members

Cyril Gabriel Alinsub
Akash Mehta
Mencia Sanchez
Steven Shan
Jackson Tubb