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

• Published in: arXiv.org
• Main Authors: Brihuega, Alberto, Abdelaziz, Mahmoud, Anttila, Lauri, Turunen, Matias, Allén, Markus, Eriksson, Thomas, Valkama, Mikko
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 30.04.2020
• Subjects: Adaptive algorithms; Algorithms; Antenna arrays; Antennas; Carrier frequencies; Complexity; Frequency ranges; Iterative methods; Linearization; Machine learning; Millimeter waves; Orthogonal Frequency Division Multiplexing; Polynomials; Waveforms
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2003.06348

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.