Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation

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 beam...

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Bibliographic Details
Published inIEEE microwave and wireless components letters Vol. 30; no. 8; pp. 843 - 846
Main Authors Brihuega, Alberto, Anttila, Lauri, Valkama, Mikko
Format Journal Article
LanguageEnglish
Published IEEE 01.08.2020
Subjects
5G new radio (NR)
Array signal processing
Artificial neural networks
digital predistortion (DPD)
Linear antenna arrays
load modulation
Loaded antennas
mmWave
neural networks (NNs)
Nonlinear distortion
Training
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Summary: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.
ISSN:1531-1309
1558-1764
DOI:10.1109/LMWC.2020.3004003