Piecewise Digital Predistortion for mmWave Active Antenna Arrays: Algorithms and Measurements

In this article, we describe a novel framework for digital predistortion (DPD)-based linearization of strongly nonlinear millimeter-wave active antenna arrays. Specifically, we formulate a piecewise (PW) closed-loop (CL) DPD solution and low-complexity gradient-adaptive parameter learning algorithms, together with a region partitioning method, which can efficiently handle deep compression of the PA units. The impact of beamsteering on the DPD performance is studied, showing strong beam-dependence, thus necessitating frequent updating of the DPD. In order to facilitate fast adaptation, an inexpensive, noniterative, pruning algorithm is introduced, which allows us to significantly reduce the number of model coefficients. The proposed methods are validated with extensive over-the-air RF measurements on a 64-element active antenna array transmitter operating at 28-GHz carrier frequency and transmitting a 400-MHz 5G new radio (NR) standard-compliant orthogonal frequency-division multiplexing waveform. The obtained results demonstrate the excellent linearization capabilities of the proposed solution, conforming to the new 5G NR requirements for frequency range 2 (FR2) in terms of both in-band waveform quality and out-of-band emissions. The proposed PW-CL DPD is shown to outperform the state-of-the-art PW DPD based on the indirect learning architecture, as well as the classical single-polynomial-based DPD solutions in terms of linearization performance and computational complexity by a clear margin.