A Simplified Predistorter for Distortion Compensation of Parallel Wiener-Type Systems Based on Direct Learning Architecture

Predistortion of parallel wiener-type systems is considered in this paper. The predistorter is first modeled as a Volterra series. In order to reduce computation complexity, a simplified predistorter model constructed using adaptive FIR filters is proposed. The coefficients of these two suggested predistorters are estimated using the nonlinear filtered-x least mean squares (NFtimesLMS) algorithm based on the direct learning architecture (DLA) approach. The simulation results show that the simplified predistorter can effectively compensate the nonlinear distortion as well as the volterra predistorter with much lower computation complexity.