Direct Learning Architectures for digital predistortion of nonlinear Volterra systems

Digital compensation of nonlinear distortion due to nonlinear characteristic of electronic or electromechanical device is becoming more and more important. This paper considers Direct Learning Architectures (DLAs) for predistortion of nonlinear systems described using Volterra series. The adaptive predistorter which is connected in tandem with the nonlinear system can be modeled as a Volterra filter or using linear and nonlinear FIR filters. Also, the coefficients of the adaptive predistorter are estimated in this paper using two approaches. The first approach is based on the Nonlinear Filtered-x Least Mean Squares (NFxLMS) algorithm. The second approach is based on using the Spectral Magnitude Matching (SMM) method that minimizes the sum squared error between spectral magnitudes of output signal of the nonlinear system and desired signal. The coefficients of the predistorter in this case are estimated recursively using the generalized Newton iterative algorithm. A comparative simulation study between these different architectures and approaches is given in this paper.