Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion
This paper explores the benefits of utilizing multistage cascaded (CC) behavioral models for digital predistortion (DPD) linearization of wideband high-efficiency power amplifiers (PAs). To reduce the computational complexity of these multistage CC behavioral models, a model-order reduction techniqu...
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| Format: | Article |
| Language: | English |
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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/10746384/ |
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| author | Raul Criado Wantao Li William Thompson Gabriel Montoro Kevin Chuang Pere L. Gilabert |
| author_facet | Raul Criado Wantao Li William Thompson Gabriel Montoro Kevin Chuang Pere L. Gilabert |
| author_sort | Raul Criado |
| collection | DOAJ |
| description | This paper explores the benefits of utilizing multistage cascaded (CC) behavioral models for digital predistortion (DPD) linearization of wideband high-efficiency power amplifiers (PAs). To reduce the computational complexity of these multistage CC behavioral models, a model-order reduction technique based on a greedy algorithm is proposed. The advantages of employing CC DPD models with gradient descent parameter identification, as opposed to single-stage DPD models with least squares parameter identification, are extensively demonstrated and analyzed. The trade-off among linearity, power efficiency and computational complexity is evaluated considering the linearization of a high-efficiency pseudo-Doherty load-modulated balanced amplifier (PD-LMBA). Using the proposed pruning strategy for CC DPD models, we demonstrate a significant reduction in the number of parameters needed to linearize the PD-LMBA. The PA operates at an RF frequency of 2 GHz and delivers a mean output power of 40 dBm with an approximately 50% power efficiency when driven by 5G new radio signals with up to 200 MHz bandwidth and an 8 dB peak-to-average power ratio. |
| format | Article |
| id | doaj-art-6dcdb27652e042e0b9b4f37129d3802b |
| institution | Kabale University |
| issn | 2692-8388 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Microwaves |
| spelling | doaj-art-6dcdb27652e042e0b9b4f37129d3802b2025-01-15T00:04:08ZengIEEEIEEE Journal of Microwaves2692-83882025-01-015113714910.1109/JMW.2024.348345810746384Model-Order Reduction of Multistage Cascaded Models for Digital PredistortionRaul Criado0https://orcid.org/0009-0006-1064-520XWantao Li1https://orcid.org/0000-0002-2634-6742William 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, Universitat Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainDepartment of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainCommunications & Cloud Business Unit, Analog Devices Inc. (ADI), Wilmington, MA, USADepartment of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainCommunications & Cloud Business Unit, Analog Devices Inc. (ADI), Wilmington, MA, USADepartment of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC) - Barcelona Tech, Castelldefels, SpainThis paper explores the benefits of utilizing multistage cascaded (CC) behavioral models for digital predistortion (DPD) linearization of wideband high-efficiency power amplifiers (PAs). To reduce the computational complexity of these multistage CC behavioral models, a model-order reduction technique based on a greedy algorithm is proposed. The advantages of employing CC DPD models with gradient descent parameter identification, as opposed to single-stage DPD models with least squares parameter identification, are extensively demonstrated and analyzed. The trade-off among linearity, power efficiency and computational complexity is evaluated considering the linearization of a high-efficiency pseudo-Doherty load-modulated balanced amplifier (PD-LMBA). Using the proposed pruning strategy for CC DPD models, we demonstrate a significant reduction in the number of parameters needed to linearize the PD-LMBA. The PA operates at an RF frequency of 2 GHz and delivers a mean output power of 40 dBm with an approximately 50% power efficiency when driven by 5G new radio signals with up to 200 MHz bandwidth and an 8 dB peak-to-average power ratio.https://ieeexplore.ieee.org/document/10746384/Cascaded behavioral modelsdigital predistortiongradient descent optimizationleast squareslinearizationload-modulated balanced amplifier |
| spellingShingle | Raul Criado Wantao Li William Thompson Gabriel Montoro Kevin Chuang Pere L. Gilabert Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion IEEE Journal of Microwaves Cascaded behavioral models digital predistortion gradient descent optimization least squares linearization load-modulated balanced amplifier |
| title | Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion |
| title_full | Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion |
| title_fullStr | Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion |
| title_full_unstemmed | Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion |
| title_short | Model-Order Reduction of Multistage Cascaded Models for Digital Predistortion |
| title_sort | model order reduction of multistage cascaded models for digital predistortion |
| topic | Cascaded behavioral models digital predistortion gradient descent optimization least squares linearization load-modulated balanced amplifier |
| url | https://ieeexplore.ieee.org/document/10746384/ |
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