AI/ML Optimized Modulations and Digital Predistortion for RF Impairments

We propose machine learning (ML) based optimization methods for modulation and digital predistortion (DPD), that overcome the signal distortion due to power amplifier (PA) non-linearity and memory effects. The proposed methods generate and exploit an adjusted modulation constellation to compensate for a given PA non-linearity, whereas conventional and widely-employed methods mainly rely on DPD to that end. This potentially removes the need for DPD if memory effects are not detrimental and enables the transmitter to operate close to the power saturation region, increasing the PA power efficiency. The ML framework to learn an adjusted constellation is trained to produce a target square quadrature amplitude modulation (QAM) signal at the PA output. We also present a DPD learning architecture for the adjusted constellations. The proposed methods outperform square 16-ary/64-ary QAMs with DPD by more than 1 dB and are within 0.2$\sim$0.3 dB of the theoretical performance at a symbol error rate of $10^{-2}$, when the PA operates in its saturation region.