Adaptive predistortion of nonlinear Volterra systems using Spectral Magnitude Matching

Digital compensation of nonlinear systems is an important topic in many practical applications. This paper considers the problem of predistortion of nonlinear systems described using Volterra series by connecting in tandem an adaptive Volterra predistorter. The suggested direct learning architecture (DLA) approach utilizes the spectral magnitude matching (SMM) method that minimizes the sum squared error between the spectral magnitudes of the output signal of the nonlinear system and the desired signal. The coefficients of the predistorter are estimated recursively using the generalized Newton iterative algorithm. A comparative simulation study with the nonlinear filtered-x least mean squares (NFxLMS) algorithm shows that the suggested SMM approach achieves much better performance but with higher computation complexity.