[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125504-en":3,"doc-seo-125504-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125504,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Predistortion of GaN Power Amplifier Transient Responses in Time-Division Duplex Using Machine Learning","Time-division duplexing (TDD) in 5G and 6G challenges the linear operation of radio base-station power amplifiers, and GaN PAs can exhibit pronounced transient behavior when resuming from idle, degrading the first transmitted symbols. This article introduces a machine-learning-based modeling and compensation approach for the PA transient, using a lightweight, low-rate recurrent model. RF measurements at 3.6 GHz validate coordinated transient compensation and predistortion, showing effective reduction of both distortion types.","Predistortion of GaN Power Amplifier Transient Responses in Time-Division Duplex Using Machine Learning  \nCitation for published version (APA):  \nFischer-Buhner, A. , Anttila, L. , Brihuega, A. , Dev Gomony, M. , & Valkama, M. (2025) . Predistortion of GaN Power Amplifier Transient Responses in Time-Division Duplex Using Machine Learning. IEEE Microwave and Wireless Technology Letters, 35(6), 924-927 . Article 10980633. [https://doi.org/10.1109/LMWT.2025.3561227](https://doi.org/10.1109/LMWT.2025.3561227)  \nDOI:  \n10.1109/LMWT.2025.3561227  \nDocument status and date:  \nPublished: 01/01/2025  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 26. Apr. 2026  \nPredistortion of GaN Power Amplifier Transient Responses in Time-Division Duplex Using  \nMachine Learning  \nArne Fischer-B¨uhner, Graduate Student Member, IEEE, Lauri Anttila, Member, IEEE, Alberto Brihuega, Manil Dev Gomony, Member, IEEE, and Mikko Valkama, Fellow, IEEE  \nAbstract—The extensive use of time-division duplexing in 5G and 6G poses a challenge to the linear operation of the power amplifiers (PAs) in radio base stations. Particularly with gallium nitride (GaN) technology, the PAs can produce strong transient behavior when resuming from an idle state, which degrades the first few transmitted symbols. This article proposes a novel machine learning technique to model and compensate the PAgain transient, based on a lightweight, low-rate recurrent model. Our RF measurements at 3.6 GHz examine the joint application of transient compensation and predistortion of short-term e􀀋ectsand show a successful mitigation of both types of distortion.  \nIndex Terms—Digital predistortion, gallium nitride (GaN) power amplifier (PA), long-term memory e􀀋ect, time-division duplex (TDD), transient response.  \nI. INTRODUCTION  \nTHE advent of 5G new radio (NR) has brought about sig  \nnificant changes to the wireless radio access network [1] . Particularly, new frequency bands and a flexible numerology and resource allocation were introduced to increase spectrum utilization and support a broad range of use cases. Among these changes, time-division duplexing (TDD) has emerged asa dominant duplexing technique for the unpaired frequency ba","cbCaijUs6svy8UkA","https://ap.wps.com/l/cbCaijUs6svy8UkA","pdf",968140,1,5,"English","en",105,"# Abstract\n# Introduction\n## Time-division duplexing and transient challenges\n## Motivation for mitigation techniques","[{\"question\":\"Why do GaN power amplifiers cause problems in time-division duplexing?\",\"answer\":\"When TDD switches quickly, GaN power amplifiers resume from an idle state with strong transient behavior, which degrades the first few transmitted symbols.\"},{\"question\":\"What machine-learning method is proposed for compensating the PA transient?\",\"answer\":\"The article proposes a machine learning technique that models and compensates the PA transient using a lightweight, low-rate recurrent model.\"},{\"question\":\"What evidence is provided to validate the approach?\",\"answer\":\"RF measurements at 3.6 GHz demonstrate successful mitigation of distortion by jointly applying transient compensation and predistortion of short-term effects.\"}]","Predistortion of GaN Power Amplifier Transient Responses in Time-Division Duplex Using Machine Learning | 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do GaN power amplifiers cause problems in time-division duplexing?","Question",{"text":75,"@type":76},"When TDD switches quickly, GaN power amplifiers resume from an idle state with strong transient behavior, which degrades the first few transmitted symbols.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine-learning method is proposed for compensating the PA transient?",{"text":80,"@type":76},"The article proposes a machine learning technique that models and compensates the PA transient using a lightweight, low-rate recurrent model.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence is provided to validate the approach?",{"text":84,"@type":76},"RF measurements at 3.6 GHz demonstrate successful mitigation of distortion by jointly applying transient compensation and predistortion of short-term 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