TY - GEN
T1 - Closed-loop sign algorithms for low-complexity digital predistortion
AU - Campo, Pablo Pascual
AU - Lampu, Vesa
AU - Anttila, Lauri
AU - Brihuega, Alberto
AU - Allen, Markus
AU - Valkama, Mikko
N1 - jufoid=70577
Funding Information:
ACKNOWLEDGMENT This work was financially supported by the Academy of Finland under the projects 301820, 323461 and 319994.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/8
Y1 - 2020/8
N2 - In this paper, we study digital predistortion (DPD) based linearization with specific focus on millimeter wave (mmW) active antenna arrays. Due to the very large channel bandwidths and beam-dependence of nonlinear distortion in such systems, we propose a closed-loop DPD learning architecture, look-up table (LUT) based memory DPD models, and low-complexity sign-based estimation algorithms, such that even continuous DPD learning could be technically feasible. To this end, three different learning algorithms - sign, signed regressor, and sign-sign - are formulated for the LUT-based DPD models, such that the potential rank deficiencies, experienced in earlier methods, are avoided. Then, extensive RF measurements utilizing a state-of-the-art mmW active antenna array system at 28 GHz are carried out and reported to validate the methods. Additionally, the processing and learning complexities of the considered methods are analyzed, which together with the measured linearization performance figures allow to assess the complexity-performance tradeoffs. Overall, the results show that efficient mmW array linearization can be obtained through the proposed methods.
AB - In this paper, we study digital predistortion (DPD) based linearization with specific focus on millimeter wave (mmW) active antenna arrays. Due to the very large channel bandwidths and beam-dependence of nonlinear distortion in such systems, we propose a closed-loop DPD learning architecture, look-up table (LUT) based memory DPD models, and low-complexity sign-based estimation algorithms, such that even continuous DPD learning could be technically feasible. To this end, three different learning algorithms - sign, signed regressor, and sign-sign - are formulated for the LUT-based DPD models, such that the potential rank deficiencies, experienced in earlier methods, are avoided. Then, extensive RF measurements utilizing a state-of-the-art mmW active antenna array system at 28 GHz are carried out and reported to validate the methods. Additionally, the processing and learning complexities of the considered methods are analyzed, which together with the measured linearization performance figures allow to assess the complexity-performance tradeoffs. Overall, the results show that efficient mmW array linearization can be obtained through the proposed methods.
KW - ACLR
KW - Array transmitters
KW - Digital predistortion
KW - Hadamard
KW - Lookup table
KW - MmW frequencies
KW - Nonlinear distortion
KW - Sign algorithm
KW - Signed regressor
U2 - 10.1109/IMS30576.2020.9223904
DO - 10.1109/IMS30576.2020.9223904
M3 - Conference contribution
AN - SCOPUS:85094185158
T3 - IEEE MTT-S International Microwave Symposium Digest
SP - 841
EP - 844
BT - IMS 2020 - 2020 IEEE/MTT-S International Microwave Symposium
PB - IEEE
T2 - IEEE/MTT-S International Microwave Symposium
Y2 - 4 August 2020 through 6 August 2020
ER -