TY - JOUR
T1 - Highly non-linear and wide-band mmWave active array OTA linearisation using neural network
AU - Jalili, Feridoon
AU - Zhang, Yufeng
AU - Hintsala, Markku
AU - Jensen, Ole Kiel
AU - Chen, Qingyue
AU - Shen, Ming
AU - Pedersen, Gert Frølund
N1 - Funding Information:
The authors would like to acknowledge Jakob G. Brask, Kasper B. Olesen and Lauge F. Dyring, all of the University of Aalborg, Denmark, for providing technical and software support during OTA measurements.
Publisher Copyright:
© 2021 The Authors. IET Microwaves, Antennas & Propagation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2022/1
Y1 - 2022/1
N2 - This paper proposes a neural network (NN)-based over-the-air (OTA) linearisation technique for a highly non-linear and wide-band mmWave active phased array (APA) transmitter and compares it with the conventional memory polynomial model (MPM)-based technique. The proposed NN effectively learns the distinctive non-linear distortions, which may not easily fit to existing MPM solutions, and can, therefore, successfully cope with the challenges introduced by the high non-linearity and wide bandwidth. The proposed technique has been evaluated using a state-of-the-art 4 × 4 APA operating in highly non-linear regions at 28 GHz with a 100-MHz-wide 3GPP base-station signal as input. Experimental results show the pre-distortion signal generated by the NN exhibits the peak-to-average power ratio (PAPR) much lower than the one generated by MPM and consequently superior linearisation performance in terms of adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) for high non-linearity cases. Using the proposed NN-based linearisation technique, an improvement of 5-dB ACLR and 7% points in EVM are achieved, which demonstrates the promising potential of this technique for emerging broadband communication systems such as 5G/6G and low Earth orbit (LEO) satellite networks.
AB - This paper proposes a neural network (NN)-based over-the-air (OTA) linearisation technique for a highly non-linear and wide-band mmWave active phased array (APA) transmitter and compares it with the conventional memory polynomial model (MPM)-based technique. The proposed NN effectively learns the distinctive non-linear distortions, which may not easily fit to existing MPM solutions, and can, therefore, successfully cope with the challenges introduced by the high non-linearity and wide bandwidth. The proposed technique has been evaluated using a state-of-the-art 4 × 4 APA operating in highly non-linear regions at 28 GHz with a 100-MHz-wide 3GPP base-station signal as input. Experimental results show the pre-distortion signal generated by the NN exhibits the peak-to-average power ratio (PAPR) much lower than the one generated by MPM and consequently superior linearisation performance in terms of adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) for high non-linearity cases. Using the proposed NN-based linearisation technique, an improvement of 5-dB ACLR and 7% points in EVM are achieved, which demonstrates the promising potential of this technique for emerging broadband communication systems such as 5G/6G and low Earth orbit (LEO) satellite networks.
KW - 5G mobile communication
KW - active antenna arrays
KW - learning (artificial intelligence)
KW - linearisation techniques
KW - millimetre wave amplifiers
KW - millimetre wave antenna arrays
KW - satellite communication
UR - http://www.scopus.com/inward/record.url?scp=85121296754&partnerID=8YFLogxK
U2 - 10.1049/mia2.12220
DO - 10.1049/mia2.12220
M3 - Journal article
AN - SCOPUS:85121296754
SN - 1751-8725
VL - 16
SP - 62
EP - 77
JO - IET Microwaves, Antennas and Propagation
JF - IET Microwaves, Antennas and Propagation
IS - 1
ER -