Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks
Mobile communication is rapidly growing. Increasing demands on capacity and bandwidth have to be addressed by future developments. This means higher signal requirements and bandwidth for transceivers in mobile basestations. Transceivers are the component with highest power consumption in a basestati...
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FAU University Press
2025
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| Առցանց հասանելիություն: | ONIX_20251215T160010_9783961476589_29 |
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Չկան պիտակներ, Եղեք առաջինը, ով նշում է այս գրառումը!
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| _version_ | 1869530505673703424 |
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| author | Jüschke, Patrick |
| author_browse | Jüschke, Patrick |
| author_facet | Jüschke, Patrick |
| author_sort | Jüschke, Patrick |
| collection | Directory of Open Access Books |
| description | Mobile communication is rapidly growing. Increasing demands on capacity and bandwidth have to be addressed by future developments. This means higher signal requirements and bandwidth for transceivers in mobile basestations. Transceivers are the component with highest power consumption in a basestation. Especially analog components show different impairments and nonideal behavior with negative effects on energy efficiency and signal integrity. These effects can be analyzed and mathematically described to build a specific digital signal processing algorithm, which mitigates certain effects. This work treats impairments from machine learning perspective. IQ Imbalance of modulators as well as power amplifier nonlinearities are representive impairments with significant influence on the signal quality. These effects are trained to artificial neural networks (ANNs) for digital impairment mitigation. Furthermore it is shown that the ANNs are able to model different impairment effects with a single network and can be simply enhanced by further input parameters to mitigate dynamic effects. Physically inspired modeling of long term memory effects like thermal memory and charge trapping are a special focus of this work. Der Bedarf an mobiler Kommunikation wächst ständig. Höhere Nachfrage nach Kapazität und Bandbreite müssen durch zukünftige Entwicklungen adressiert werden. Um diese Zielen zu erreichen sind Sende- und Empfangseinheiten für höhere Signalanforderungen und Bandbreiten für Mobilfunkbasisstationen erforderlich. Diese Einheiten verbrauchen die meiste Energie in Basisstationen. Vor allem analoge Komponenten beeinträchtigen die Signalqualität und haben Einfluss auf die Energieeffizienz. Diese Effekte können analysiert und mathematisch in einem digitalen Signalverarbeitungsalgorithmus beschrieben werden um diese Effekte vor zu verzerren und damit abzuschwächen. Diese Arbeit betrachtet diese Effekte aus der Perspektive des maschinellen Lernens. IQ Imbalanz und Nichtlinearitäten von Leistungsverstärkern sind repräsentative Effekte mit großen Einfluss auf die Signalqualität. Diese Effekte werden zur digitalen Vorverzerrung mit künstlichen neuronalen Netzen (KNN) trainiert. Zudem wird gezeigt, das KNN dazu in der Lage sind, mehrere Effekte mit einem Modell abzubilden. Physikalisch inspirierte Modellierung von Langzeiteffekten mit neuronalen Netzen wie dem thermischen Gedächtnis oder Ladungsfallen stehen im besonderen Mittelpunkt dieser Arbeit. |
| format | Online |
| id | doab-20.500.12854ir-170246 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | FAU University Press |
| publisherStr | FAU University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1702462025-12-16T05:35:04Z Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks Jüschke, Patrick Leistungsverstärker Maschinelles Lernen Hochfrequenztechnik Digitale Vorverzerrung Künstliche Intelligenz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKH Signal processing thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKR Radio technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKW WAP (wireless) technology thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTW WAP networking and applications Mobile communication is rapidly growing. Increasing demands on capacity and bandwidth have to be addressed by future developments. This means higher signal requirements and bandwidth for transceivers in mobile basestations. Transceivers are the component with highest power consumption in a basestation. Especially analog components show different impairments and nonideal behavior with negative effects on energy efficiency and signal integrity. These effects can be analyzed and mathematically described to build a specific digital signal processing algorithm, which mitigates certain effects. This work treats impairments from machine learning perspective. IQ Imbalance of modulators as well as power amplifier nonlinearities are representive impairments with significant influence on the signal quality. These effects are trained to artificial neural networks (ANNs) for digital impairment mitigation. Furthermore it is shown that the ANNs are able to model different impairment effects with a single network and can be simply enhanced by further input parameters to mitigate dynamic effects. Physically inspired modeling of long term memory effects like thermal memory and charge trapping are a special focus of this work. Der Bedarf an mobiler Kommunikation wächst ständig. Höhere Nachfrage nach Kapazität und Bandbreite müssen durch zukünftige Entwicklungen adressiert werden. Um diese Zielen zu erreichen sind Sende- und Empfangseinheiten für höhere Signalanforderungen und Bandbreiten für Mobilfunkbasisstationen erforderlich. Diese Einheiten verbrauchen die meiste Energie in Basisstationen. Vor allem analoge Komponenten beeinträchtigen die Signalqualität und haben Einfluss auf die Energieeffizienz. Diese Effekte können analysiert und mathematisch in einem digitalen Signalverarbeitungsalgorithmus beschrieben werden um diese Effekte vor zu verzerren und damit abzuschwächen. Diese Arbeit betrachtet diese Effekte aus der Perspektive des maschinellen Lernens. IQ Imbalanz und Nichtlinearitäten von Leistungsverstärkern sind repräsentative Effekte mit großen Einfluss auf die Signalqualität. Diese Effekte werden zur digitalen Vorverzerrung mit künstlichen neuronalen Netzen (KNN) trainiert. Zudem wird gezeigt, das KNN dazu in der Lage sind, mehrere Effekte mit einem Modell abzubilden. Physikalisch inspirierte Modellierung von Langzeiteffekten mit neuronalen Netzen wie dem thermischen Gedächtnis oder Ladungsfallen stehen im besonderen Mittelpunkt dieser Arbeit. 2025-12-16T05:35:03Z 2025-12-16T05:35:03Z 2025-12-15T15:03:43Z 2023 book ONIX_20251215T160010_9783961476589_29 https://library.oapen.org/handle/20.500.12657/109149 9783961476589 9783961476572 https://directory.doabooks.org/handle/20.500.12854/170246 eng FAU Studien aus der Elektrotechnik open access image/jpeg Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/109149/1/9783961476589.pdf FAU University Press 10.25593/978-3-96147-658-9 10.25593/978-3-96147-658-9 2c600dea-eece-4066-87be-da335e323fdb 9783961476589 9783961476572 132 Erlangen open access |
| spellingShingle | Leistungsverstärker Maschinelles Lernen Hochfrequenztechnik Digitale Vorverzerrung Künstliche Intelligenz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKH Signal processing thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKR Radio technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKW WAP (wireless) technology thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTW WAP networking and applications Jüschke, Patrick Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title | Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title_full | Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title_fullStr | Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title_full_unstemmed | Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title_short | Physically Inspired Predistortion of RF Power Amplifiers with Artificial Neural Networks |
| title_sort | physically inspired predistortion of rf power amplifiers with artificial neural networks |
| topic | Leistungsverstärker Maschinelles Lernen Hochfrequenztechnik Digitale Vorverzerrung Künstliche Intelligenz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKH Signal processing thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKR Radio technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKW WAP (wireless) technology thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTW WAP networking and applications |
| topic_facet | Leistungsverstärker Maschinelles Lernen Hochfrequenztechnik Digitale Vorverzerrung Künstliche Intelligenz thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKH Signal processing thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKR Radio technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TJ Electronics and communications engineering::TJK Communications engineering / telecommunications::TJKW WAP (wireless) technology thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTW WAP networking and applications |
| url | ONIX_20251215T160010_9783961476589_29 |
| work_keys_str_mv | AT juschkepatrick physicallyinspiredpredistortionofrfpoweramplifierswithartificialneuralnetworks |