Higher locality non-linear basis functions of Volterra series based models to improve extraction conditioning

Extraction of Volterra based nonlinear system's models can be hindered by conditioning problems. This is especially true when working with low precision arithmetic, typical of hardware implemented DPD systems. Traditionally, monomials are chosen as the basis functions because they are continuous and easy to manipulate, although other bases could be used. Spline bases, for example, have been chosen instead of polynomials for their increased locality. However, we now show that it is also possible to construct polynomials with increased locality and thus, keeping continuity but still improving the condition numbers of the regression matrix. This polynomial basis can be seen as a bridge between LUT and polynomial models.