TY - JOUR
T1 - A two-step approach for DLA-based digital predistortion using an integrated neural network
AU - Jung, Sunghoon
AU - Kim, Yeonghwan
AU - Woo, Youngyun
AU - Lee, Chungyong
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - In this study, we propose a two-step approach for direct-learning-architecture (DLA) based digital predistortion (DPD) using an integrated neural network. Because solutions to the DLA-based DPD cannot be obtained in closed form, an iterative search, which causes performance degradation and DPD divergence, is required. The proposed method employs an integrated neural network combining two sub-networks, namely, a DPD network and a power amplifier (PA) network, to find unknown solution. A one-dimensional convolutional neural network is adopted as the base structure for the DPD network to consider memory effects. The experimental results demonstrate that the proposed method reduces the adjacent channel leakage ratio by 4.3 dB and the error vector magnitude by 0.07, compared to the conventional method, and is stable over a long period without DPD coefficient update.
AB - In this study, we propose a two-step approach for direct-learning-architecture (DLA) based digital predistortion (DPD) using an integrated neural network. Because solutions to the DLA-based DPD cannot be obtained in closed form, an iterative search, which causes performance degradation and DPD divergence, is required. The proposed method employs an integrated neural network combining two sub-networks, namely, a DPD network and a power amplifier (PA) network, to find unknown solution. A one-dimensional convolutional neural network is adopted as the base structure for the DPD network to consider memory effects. The experimental results demonstrate that the proposed method reduces the adjacent channel leakage ratio by 4.3 dB and the error vector magnitude by 0.07, compared to the conventional method, and is stable over a long period without DPD coefficient update.
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U2 - 10.1016/j.sigpro.2020.107736
DO - 10.1016/j.sigpro.2020.107736
M3 - Article
AN - SCOPUS:85089348286
VL - 177
JO - Signal Processing
JF - Signal Processing
SN - 0165-1684
M1 - 107736
ER -