Closed-Loop Sign Algorithms for Low-Complexity Digital Predistortion: Methods and Performance

In this article, we study digital predistortion (DPD)based linearization with a specific focus on millimeter-wave (mmW) active antenna arrays. Due to the very large-channel bandwidths and beam-dependence of nonlinear distortion in such systems, we present a closed-loop DPD learning architecture, lookup table (LUT)-based memory DPD models, and low-complexity sign-based estimation algorithms such that even continuous DPD learning could be technically feasible. To this end, three different learning algorithms-sign, signed regressor, and sign-sign-are formulated for the LUT-based DPD models such that the potential rank deficiencies, experienced in earlier methods, are avoided while facilitating greatly reduced learning complexity. The injection-based LUT DPD structure is also shown to allow for low numbers and reduced dynamic range of the involved LUT entries. Extensive RF measurements utilizing a state-of-the-art mmW active antenna array system at 28 GHz are carried out and reported to validate the methods, incorporating very wide channel bandwidths of 400 and 800 MHz while pushing the array close to saturation. In addition, the processing and learning complexities of the considered techniques are analyzed, which, together with the measured linearization performance figures, allows to assess the complexity-performance tradeoffs of the proposed solutions. Overall, the results show that efficient mmW array linearization can be obtained through the proposed methods at very low complexity.

Automated summary

This article studies digital predistortion-based linearization with a specific focus on millimeter-wave active antenna arrays, proposing closed-loop DPD learning architecture and memory DPD models based on lookup tables (LUT) to achieve technically feasible continuous DPD learning. It also introduces low-complexity sign-based estimation algorithms to avoid potential rank deficiencies and reduce learning complexity.