Heuristic Model Structure Optimization for Digital Predistortion

Power amplifiers (PAs) are widely used in RF broadcasting applications. However, they exhibit nonlinear behavior and deteriorate the quality of transmitted signals. Digital predistortion (DPD) is developed to linearize the distortion generated by PAs. Due to the contradiction between modeling precision and complexity, to reduce the complexity and ensure comparable performance, it is necessary to determine an appropriate or optimized structure before applying DPD. Heuristic method is a good method to solve such multivariate problems with a considerable large search domain. In this paper, a novel approach to determine the structure of DPD based on heuristic method is proposed, including the enhanced hill-climbing (EHC) with stronger global search ability and enhanced genetic algorithm (EGA). Ridge regression is introduced to ensure the correctness of the search direction. Besides, orthogonal matching pursuit is used to further reduce the basis number while keeping good performance. The validation of the proposed techniques on a Doherty PA using generalized memory polynomial (GMP) model demonstrates the capability to efficiently find the dimensions of an appropriate GMP model. As shown by the convergence curve and the final model performance, the model searched by the proposed method has satisfactory results, and achieves a good balance between complexity and performance.