Low Complexity Digital Predistortion for Power Amplifiers Using Incremental PCA

Abstract

Digital predistortion (DPD) compensates for nonlinear distortions caused by RF power amplifiers (PAs) for efficient and linear signal transmission. Although generalized memory polynomial (GMP) models are frequently used for DPD, their dimensionality increases with memory depth, which raises computational costs and deteriorates numerical conditioning. Although batch principal component analysis (PCA) reduce this dimensionality, it is unable to adjust to PA characteristics that change over time. We present IPCA-GMP, a framework that combines GMP modelling with incremental PCA (IPCA), where updating mean and covariance estimates recursively and executing rank one eigenspace updates from streaming data. The eigenspace update achieves $\mathcal{O}\left(k D^{2}\right)$ per block cost without a complete eigen decomposition by using a rank one perturbation procedure on the current eigen basis. The results demonstrate that IPCA-GMP compresses the model dimension from $D=224$ to $k=20 (11 \times$ compression), achieving a normalized mean square error of -39.02 dB within 0.41, dB of the full GMP baseline. The condition number is better than $3.3 \times 10^{26}$ to 1,570, and with just 1.8% FLOP overhead compared to the baseline GMP solver, the per-update complexity drops from $\mathcal{O}\left(N D^{2}+D^{3}\right)$ (batch PCA) to $\mathcal{O}\left(D^{2}+k D^{2}\right)$.