In this letter, we propose an efficient solution to linearize mmWave active antenna array transmitters that suffer from beam-dependent load modulation. We consider a dense neural network that is capable of modeling the correlation between the nonlinear distortion characteristics among different beams. This allows providing consistently good linearization regardless of the beamforming direction, thus avoiding the necessity of executing continuous digital predistortion parameter learning. The proposed solution is validated, conforming to 5G new radio transmit signal quality requirements, with extensive over-the-air RF measurements utilizing a state-of-the-art 64-element active antenna array operating at 28-GHz carrier frequency.

中文翻译：

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负载调制下有源天线阵列的基于神经网络的数字预失真

在这封信中，我们提出了一种有效的解决方案来线性化受到波束相关负载调制影响的毫米波有源天线阵列发射器。我们考虑一个密集的神经网络，它能够对不同光束之间的非线性失真特性之间的相关性进行建模。无论波束形成方向如何，这都允许提供始终如一的良好线性化，从而避免执行连续数字预失真参数学习的必要性。所提议的解决方案经过验证，符合 5G 新无线电发射信号质量要求，并利用在 28 GHz 载波频率下运行的最先进的 64 单元有源天线阵列进行广泛的空中 RF 测量。