A General Order Reduction Method of Wideband Digital Predistortion Model Using Attention Mechanism

Abstract

In wireless networks, for the common in-phase and quadrature-phase ( $I$ / $Q$ ) imbalance in the transmitters, the $I$ / $Q$ branch models of digital predistortion (DPD) need to be identified separately, to improve the linearization effects. The existing order reduction methods of the predistorter are based on the contributions of the complex basis function terms, so as not to deal with the different contributions of $I$ / $Q$ components of the complex basis function terms caused by the separate identification of the $I$ / $Q$ branch models. The separate pruning of the $I$ / $Q$ branch models will increase the complexity. Aiming at this issue, this paper proposes a general order reduction method based on the attention mechanism for the predistortion of the power amplifiers (PAs). This method is suitable for pruning both the traditional models and neural network-based models. In this method, the attention mechanism is used to evaluate the contributions of the real basis function terms to the predistorted output’s $I$ / $Q$ components through offline training, and the influence of the cross terms of the $I$ / $Q$ branch models is considered. The experimental results based on the comparison with other typical methods under 100 MHz Doherty PA and different $I$ / $Q$ imbalance levels show that this method has superior pruning performance and good linearization ability.