A Novel Algorithm for Determining the Structure of Digital Predistortion Models

Abstract

The generalized memory polynomial (GMP) model is one of the most commonly used models in digital predistortion to compensate for the nonlinearities and memory effects of power amplifiers (PAs). A very difficult, yet crucial, aspect of behavioral modeling is model dimensioning, i.e., determining nonlinear orders and memory depths. In this paper, we propose an algorithm based on hill-climbing (HC) heuristic to search for a GMP model structure, which provides the best tradeoff between modeling accuracy and complexity with different search criteria. Algorithm performance and convergence are evaluated for two different search criteria. Some optimization methods of the algorithms are then proposed to reduce the execution time by controlling its search path. The algorithm has been evaluated using real measurements from two different Doherty PAs. The solutions of the algorithm are proved robust in linearization of PA on the test bench. The results show that the proposed algorithm allows us to decrease the searching time by a factor typically greater than $10^4$ compared with exhaustive search.