  In this paper, 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, that 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,
non-iterative, pruning algorithm is introduced, which allows to significantly
reduce the amount 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 inband 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.
