GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers
This paper presents a feature selection technique based on <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> regularization to select the most relevant weights of artificial neural networks (ANNs) for digital predistortion (DPD) line...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Microwaves |
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| Online Access: | https://ieeexplore.ieee.org/document/10994208/ |
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| author | Wantao Li Raul Criado William Thompson Gabriel Montoro Kevin Chuang Pere L. Gilabert |
| author_facet | Wantao Li Raul Criado William Thompson Gabriel Montoro Kevin Chuang Pere L. Gilabert |
| author_sort | Wantao Li |
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| description | This paper presents a feature selection technique based on <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> regularization to select the most relevant weights of artificial neural networks (ANNs) for digital predistortion (DPD) linearization of wideband radio-frequency (RF) power amplifiers (PAs). The proposed pruning method is applied to the first hidden layer of a feed-forward real-valued time-delay neural network, commonly used for DPD purposes. In addition, this paper presents the ANN-based DPD implementation using a graphic processing unit (GPU) with compute unified device architecture (CUDA) units. Thanks to the proposed pruning strategy, it is possible to reduce the ANN complexity significantly, thereby achieving a higher data throughput with the GPU-based implementation. The trade-off among RF performance metrics, number of model parameters and throughput of the GPU implementation is evaluated considering the linearization of a high-efficiency pseudo-Doherty load modulated balanced amplifier (LMBA). The linearized PA operating at an RF frequency of 2 GHz delivers a mean output power of 40 dBm with approximately 50% power efficiency when excited with 5G new radio (NR) signals with up to 200 MHz bandwidth and an 8 dB peak-to-average power ratio (PAPR). The real-time GPU implementation of the ANN-based DPD can meet the linearity specifications with a throughput circa 1 GSa/s. |
| format | Article |
| id | doaj-art-fe5faae54f8e45ae8992a409a53fd607 |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Journal of Microwaves |
| spelling | doaj-art-fe5faae54f8e45ae8992a409a53fd6072025-08-20T02:28:18ZengIEEEIEEE Journal of Microwaves2692-83882025-01-015372673810.1109/JMW.2025.356042010994208GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power AmplifiersWantao Li0https://orcid.org/0000-0002-2634-6742Raul Criado1https://orcid.org/0009-0006-1064-520XWilliam Thompson2Gabriel Montoro3https://orcid.org/0000-0002-1328-4175Kevin Chuang4https://orcid.org/0000-0003-0805-5519Pere L. Gilabert5https://orcid.org/0000-0001-6183-6977Department of Signal Theory and Communications, University of Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainDepartment of Signal Theory and Communications, University of Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainAerospace, Defense and Communications Business Unit, Analog Devices Inc. (ADI), Wilmington, MA, USADepartment of Signal Theory and Communications, University of Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainAerospace, Defense and Communications Business Unit, Analog Devices Inc. (ADI), Wilmington, MA, USADepartment of Signal Theory and Communications, University of Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainThis paper presents a feature selection technique based on <inline-formula><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> regularization to select the most relevant weights of artificial neural networks (ANNs) for digital predistortion (DPD) linearization of wideband radio-frequency (RF) power amplifiers (PAs). The proposed pruning method is applied to the first hidden layer of a feed-forward real-valued time-delay neural network, commonly used for DPD purposes. In addition, this paper presents the ANN-based DPD implementation using a graphic processing unit (GPU) with compute unified device architecture (CUDA) units. Thanks to the proposed pruning strategy, it is possible to reduce the ANN complexity significantly, thereby achieving a higher data throughput with the GPU-based implementation. The trade-off among RF performance metrics, number of model parameters and throughput of the GPU implementation is evaluated considering the linearization of a high-efficiency pseudo-Doherty load modulated balanced amplifier (LMBA). The linearized PA operating at an RF frequency of 2 GHz delivers a mean output power of 40 dBm with approximately 50% power efficiency when excited with 5G new radio (NR) signals with up to 200 MHz bandwidth and an 8 dB peak-to-average power ratio (PAPR). The real-time GPU implementation of the ANN-based DPD can meet the linearity specifications with a throughput circa 1 GSa/s.https://ieeexplore.ieee.org/document/10994208/Artificial neural networkdigital predistortiongraphics processing unitload-modulated balanced amplifiermodel-order reduction |
| spellingShingle | Wantao Li Raul Criado William Thompson Gabriel Montoro Kevin Chuang Pere L. Gilabert GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers IEEE Journal of Microwaves Artificial neural network digital predistortion graphics processing unit load-modulated balanced amplifier model-order reduction |
| title | GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers |
| title_full | GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers |
| title_fullStr | GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers |
| title_full_unstemmed | GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers |
| title_short | GPU-Based Implementation of Pruned Artificial Neural Networks for Digital Predistortion Linearization of Wideband Power Amplifiers |
| title_sort | gpu based implementation of pruned artificial neural networks for digital predistortion linearization of wideband power amplifiers |
| topic | Artificial neural network digital predistortion graphics processing unit load-modulated balanced amplifier model-order reduction |
| url | https://ieeexplore.ieee.org/document/10994208/ |
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