Experimental demonstration of a machine learning-based piece-wise digital predistortion method in 5G NR systems

This paper demonstrates a piece-wise digital predistortion (PW-DPD) for a power amplifier (PA) in 5G new radio (NR) systems. It involves modeling the digital predistorter based on the machine learning (ML) classification of the operational states. The experimental results demonstrate that by extracting some key features from 5G NR signal statistics and the PA operating point can offer better PA linearization performance/complexity tradeoff than the conventional approach based on a single pruned Volterra model. The proposed approach is validated by laboratory experiments and shows up to 3.5 dB error vector magnitude (EVM) improvement over the conventional approach for a class A PA at 28 GHz.