ZTE Communications ›› 2025, Vol. 23 ›› Issue (1): 71-77.DOI: 10.12142/ZTECOM.202501009
• Research Papers • Previous Articles Next Articles
SHAO Jianfeng1(), HONG Xi1, WANG Wenjie1, LIN Zeyu2, LI Yunhua2
Received:
2023-09-23
Online:
2025-03-25
Published:
2025-03-25
About author:
SHAO Jianfeng (sjf1996717@stu.xjtu.edu.cn) received his BS degree in electronic information engineering from the School of Information Engineering, North China University of Water Resources and Electric Power in 2018, MS degree in circuits and systems from the School of Physics and Electronic Engineering, Ningxia University, China in 2021. He is pursuing a PhD degree in information and communication engineering at Xi'an Jiaotong University, China. His research interests include array signal processing and digital pre-distortion.Supported by:
SHAO Jianfeng, HONG Xi, WANG Wenjie, LIN Zeyu, LI Yunhua. A Basis Function Generation Based Digital Predistortion Concurrent Neural Network Model for RF Power Amplifiers[J]. ZTE Communications, 2025, 23(1): 71-77.
Datasets | ACPR of Left Band/dB | ACPR of Right Band/dB | NMSE | ||
---|---|---|---|---|---|
100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | 100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | ||
300 MHz valset | -54.17 | -46.18 | -58.04 | -48.65 | |
300 MHz datasets | Max=-21.45 Min=-21.49 Average=-21.47 | Max=-21.00 Min=-21.02 Average=-21.00 | Max=-21.94 Min=-21.98 Average=-21.96 | Max=-21.01 Min=-21.01 Average=-21.01 | Max=-16.40 Min=-16.41 Average=-16.40 |
400 MHz valset | -54.90 | -43.29 | -53.61 | -46.86 | |
400 MHz datasets | Max=-21.36 Min=-21.40 Average=-21.38 | Max=-19.39 Min=-19.48 Average=-9.45 | Max=-22.61 Min=-22.88 Average=-22.66 | Max=-20.13 Min=-20.22 Average=-20.19 | Max=-17.02 Min=-17.05 Average=-17.04 |
Table 1 Frequency domain performance of datasets
Datasets | ACPR of Left Band/dB | ACPR of Right Band/dB | NMSE | ||
---|---|---|---|---|---|
100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | 100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | ||
300 MHz valset | -54.17 | -46.18 | -58.04 | -48.65 | |
300 MHz datasets | Max=-21.45 Min=-21.49 Average=-21.47 | Max=-21.00 Min=-21.02 Average=-21.00 | Max=-21.94 Min=-21.98 Average=-21.96 | Max=-21.01 Min=-21.01 Average=-21.01 | Max=-16.40 Min=-16.41 Average=-16.40 |
400 MHz valset | -54.90 | -43.29 | -53.61 | -46.86 | |
400 MHz datasets | Max=-21.36 Min=-21.40 Average=-21.38 | Max=-19.39 Min=-19.48 Average=-9.45 | Max=-22.61 Min=-22.88 Average=-22.66 | Max=-20.13 Min=-20.22 Average=-20.19 | Max=-17.02 Min=-17.05 Average=-17.04 |
Datasets | ACPR of Left Band/dB | ACPR of Right Band/dB | NMSE | ||
---|---|---|---|---|---|
100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | 100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | ||
300 MHz trainsets | Max=-50.27 Min=-50.77 Average=-50.48 | Max=-42.53 Min=-43.15 Average=-42.94 | Max=-51.29 Min=-51.99 Average=-51.70 | Max=-44.25 Min=-45.14 Average=-44.73 | Max=-43.62 Min=-46.12 Average=-46.69 |
300 MHz testsets | -49.65, -49.48 | -42.77, -42.57 | -50.71, -50.69 | -44.35, -44.25 | -44.77, -44.79 |
400 MHz trainsets | Max=-48.93 Min=-49.36 Average=-49.17 | Max=-40.99 Min=-41.49 Average=-41.24 | Max=-48.66 Min=-49.10 Average=-48.82 | Max=-42.45 Min=-43.40 Average=-43.10 | Max=-43.86 Min=-44.31 Average=-44.15 |
400 MHz testsets | -48.02, -48.17 | -40.50, -40.64 | -48.13, -48.12 | -42.44, -42.54 | -42.92, -42.85 |
Table 2 Frequency domain performance of model output
Datasets | ACPR of Left Band/dB | ACPR of Right Band/dB | NMSE | ||
---|---|---|---|---|---|
100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | 100 MHz Integral Bandwidth | 20 MHz Integral Bandwidth | ||
300 MHz trainsets | Max=-50.27 Min=-50.77 Average=-50.48 | Max=-42.53 Min=-43.15 Average=-42.94 | Max=-51.29 Min=-51.99 Average=-51.70 | Max=-44.25 Min=-45.14 Average=-44.73 | Max=-43.62 Min=-46.12 Average=-46.69 |
300 MHz testsets | -49.65, -49.48 | -42.77, -42.57 | -50.71, -50.69 | -44.35, -44.25 | -44.77, -44.79 |
400 MHz trainsets | Max=-48.93 Min=-49.36 Average=-49.17 | Max=-40.99 Min=-41.49 Average=-41.24 | Max=-48.66 Min=-49.10 Average=-48.82 | Max=-42.45 Min=-43.40 Average=-43.10 | Max=-43.86 Min=-44.31 Average=-44.15 |
400 MHz testsets | -48.02, -48.17 | -40.50, -40.64 | -48.13, -48.12 | -42.44, -42.54 | -42.92, -42.85 |
1 | LIU Z J, HU X, WANG W D, et al. A joint PAPR reduction and digital predistortion based on real-valued neural networks for OFDM systems [J]. IEEE transactions on broadcasting, 2022, 68(1): 223–231. DOI: 10.1109/TBC.2021.3132158 |
2 | LIU Z J, HU X, XU L X, et al. Low computational complexity digital predistortion based on convolutional neural network for wideband power amplifiers [J]. IEEE transactions on circuits and systems II: express briefs, 2022, 69(3): 1702–1706. DOI: 10.1109/TCSII.2021.3109973 |
3 | LIU X, CHEN W H, WANG D H, et al. Robust digital predistortion for LTE/5G power amplifiers utilizing negative feedback iteration [J]. ZTE communications, 2020, 18(3): 49–56. DOI: 10.12142/ZTECOM.202003008 |
4 | LIU Y J, ZHOU J, CHEN W H, et al. A robust augmented complexity-reduced generalized memory polynomial for wideband RF power amplifiers [J]. IEEE transactions on industrial electronics, 2014, 61(5): 2389–2401. DOI: 10.1109/TIE.2013.2270217 |
5 | ZHU A D, DRAXLER P J, YAN J J, et al. Open-loop digital predistorter for RF power amplifiers using dynamic deviation reduction-based Volterra series [J]. IEEE transactions on microwave theory and techniques, 2008, 56(7): 1524–1534. DOI: 10.1109/TMTT.2008.925211 |
6 | BRAITHWAITE R N. Adaptation of a digitally predistorted RF amplifier using selective sampling [J]. ZTE communications, 2011, 9(3): 3–12 |
7 | HU X, LIU Z J, WANG W D, et al. Low-feedback sampling rate digital predistortion using deep neural network for wideband wireless transmitters [J]. IEEE transactions on communications, 2020, 68(4): 2621–2633. DOI: 10.1109/TCOMM.2020.2966718 |
8 | ROSOŁOWSKI D W, JĘDRZEJEWSKI K. Experimental evaluation of PA digital predistortion based on simple feedforward neural network [C]//Proc. 23rd International Microwave and Radar Conference (MIKON). IEEE, 2020: 293–296. DOI: 10.23919/ MIKON48703.2020.9253814 |
9 | FENG X, FEUVRIE B, DESCAMPS A S, et al. Digital predistortion method combining memory polynomial and feed-forward neural network [J]. Electronics letters, 2015, 51(12): 943–945. DOI: 10.1049/el.2015.0276 |
10 | WU H B, CHEN W H, LIU X, et al. A uniform neural network digital predistortion model of RF power amplifiers for scalable applications [J]. IEEE transactions on microwave theory and techniques, 2022, 70(11): 4885–4899. DOI: 10.1109/TMTT.2022.3205930 |
11 | JUNG S, KIM Y, WOO Y, et al. A two-step approach for DLA-based digital predistortion using an integrated neural network [J]. Signal processing, 2020, 177: 107736. DOI: 10.1016/j.sigpro.2020.107736 |
12 | WU Y B, GUSTAVSSON U, AMAT A G I, et al. Residual neural networks for digital predistortion [C]//Proc. 2020 IEEE Global Communications Conference. IEEE, 2020: 1-6. DOI: 10.1109/globecom42002.2020.9322327 |
13 | LÓPEZ-BUENO D, MONTORO G, GILABERT P L. Training data selection and dimensionality reduction for polynomial and artificial neural network MIMO adaptive digital predistortion [J]. IEEE transactions on microwave theory and techniques, 2022, 70(11): 4940–4954. DOI: 10.1109/TMTT.2022.3209214 |
14 | JIANG C Y, YANG G C, HAN R L, et al. Gated dynamic neural network model for digital predistortion of RF power amplifiers with varying transmission configurations [J]. IEEE transactions on microwave theory and techniques, 2023, 71(8): 3605–3616. DOI: 10.1109/TMTT.2023.3241612 |
15 | JARAUT P, ABDELHAFIZ A, CHENINI H, et al. Augmented convolutional neural network for behavioral modeling and digital predistortion of concurrent multiband power amplifiers [J]. IEEE transactions on microwave theory and techniques, 2021, 69(9): 4142–4156. DOI: 10.1109/TMTT.2021.3075689 |
16 | JIANG C Y, LI H M, QIAO W, et al. Block-oriented time-delay neural network behavioral model for digital predistortion of RF power amplifiers [J]. IEEE transactions on microwave theory and techniques, 2022, 70(3): 1461–1473. DOI: 10.1109/TMTT.2021.3124211 |
17 | KOBAL T, LI Y, WANG X Y, et al. Digital predistortion of RF power amplifiers with phase-gated recurrent neural networks [J]. IEEE transactions on microwave theory and techniques, 2022, 70(6): 3291–3299. DOI: 10.1109/TMTT.2022.3161024 |
18 | MONDAL R, RISTANIEMI T, DOULA M. Genetic algorithm optimized memory polynomial digital pre-distorter for RF power amplifiers[C]//Proc. International Conference on Wireless Communications and Signal Processing. IEEE, 2013: 1–5. DOI: 10.1109/WCSP.2013.6677117 |
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