mmWave有源天线阵的分段数字预失真：算法与测量
Piecewise Digital Predistortion fo mmWave Active Antenna Arrays: Algorithms and Measurements
摘要：
在本文中，我们描述了一种新的框架数字预失真（DPD）的线性化强非线性毫米波有源天线阵列。具体来说，我们制定了一个分段（PW）闭环（CL）DPD解决方案和低复杂度的梯度自适应参数学习算法，以及区域划分方法，可以有效地处理深度压缩的PA单元。波束控制对DPD性能的影响进行了研究，表现出很强的波束依赖性，因此需要频繁更新的DPD。为了便于快速适应，一个廉价的，非迭代的，修剪算法，它允许显着减少模型系数的量。所提出的方法通过在64元件有源天线阵列发射机上进行广泛的空中RF测量进行了验证，该发射机工作在28 GHz载波频率下，并传输符合400 MHz 5G新无线电（NR）标准的正交频分复用波形。所获得的结果表明，所提出的解决方案具有出色的线性化能力，在带内波形质量和带外发射方面均符合频率范围2（FR 2）的新5G NR要求。所提出的PW-CL DPD的表现优于国家的最先进的PW DPD的基础上的间接学习架构，以及经典的单多项式为基础的DPD解决方案的线性化性能和计算复杂性的一个明确的保证金。
英文摘要：
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.
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