Optimal Sizing of Two-Stage Cascaded Sparse Memory Polynomial Model for High Power Amplifiers Linearization

Abstract

The nonlinearities and memory effects of power amplifiers (PAs) can be compensated by multistage cascaded digital predistortion with low complexity. Compared with full multistage models, sparse multistage models may have the same linearization performances while their complexities are even lower. However, the choice of the model structure is very difficult, especially for the sparse models. For instance, if there are (K+1)(L+1) full models, then 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K+L-2</sup> different corresponding sparse models exist. An algorithm with the optimal search space definition is proposed in this paper to search for the optimal cascaded sparse model structure. The search criterion represents tradeoff between the modeling accuracy and identification complexity with a weight coefficient. A method to determine the value of the weight coefficient is proposed in this paper. The sparse model solution found by the proposed algorithm is evaluated with a three-way Doherty PA using a long term evolution-advanced signal. It is also compared with the solution of full model structure search.