Robust Digital Pre-Distortion Parameter Estimation with Interpretable Feature Selection

Batch estimation using Least Squares (LS) is commonly used to calculate DPD coefficients. However, non-orthogonal polynomial-based structures in DPD lead to multicollinearity, causing ill conditioning in coefficient calculation. This paper introduces an improved Ridge Regression technique for digital pre-distortion (DPD) parameter reduction. Using Ridge Leverage, we identify DPD basis functions with minimal impact on linearization performance, achieving a 39% reduction in DPD coefficients.