A New Sparse Design Framework For Broadband Power Amplifier Behavioral Modeling And Digital Predistortion

In this paper, we propose a new sparse framework for the design of the behavioral model and digital predistorter of a broadband power amplifier (PA). We start by formulating the Volterra kernel to multidimensional memory polynomial by considering the high-order dynamic truncation of the Volterra model. Then we show how an estimate of the most significant coefficients may be obtained using a matching pursuit (MPT) algorithm by exploiting the sparsity of the model. After the indices of the nonzero coefficients are roughly estimated, the block exact Householder inverse QR-decomposition-based recursive least squares (HIQRD-RLS) algorithm is utilized to estimate the sparse model complex coefficients. For broadband nonlinear PAs, the proposed approach is demonstrated to achieve the best performance among the well-known traditional approaches in terms of in-band and out-of-band specifications. The proposed approach is also validated by evaluating the digital predistortion (DPD) performance on a Class-AB PA in terms of adjacent channel power ratio (ACPR). (C) 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.