[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127918-en":3,"doc-seo-127918-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127918,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Machine learning-based methods for piecewise digital predistortion in mmW 5G NR systems - Research Open Access","Piecewise linearization techniques split a signal into multiple segments, each linearized independently, and machine learning automates this partitioning. The study reduces classification complexity by designing features from both signal statistics and power amplifier characteristics. Two low-complexity classical ML methods classify baseband input data into distinct segments and linearize each segment using tailored Volterra models. Analyses refine classification and regression complexities, with lab experiments showing up to 4 dB EVM improvement over a conventional method for a class A PA at 28 GHz, and up to 2 dB over a vector-switched generalized memory polynomial scheme.","Bulusu et al.  \nEURASIP Journal on Advances in Signal Processing [https://doi.org/10.1186/s13634-024-01191-7](https://doi.org/10.1186/s13634-024-01191-7)  \n(2024) 2024:97  \nEURASIP Journal on Advancesin Signal Processing  \nRESEARCH Open Access  \nMachine learning-based methods  \nfor piecewise digital predistortion in mmW 5G NR systems  \nS. S. Krishna Chaitanya Bulusu 1*, Nuutti Tervo1, Praneeth Susarla2, Olli Silvén2, Mikko. J. Sillanpää3, Marko E. Leinonen 1, Markku Juntti1 and Aarno Pärssinen 1  \n*Correspondence: [sri.bulusu@oulu.fi](sri.bulusu@oulu.fi)  \n1 Centre for  \nWireless Communications (CWC), University of Oulu, Pentti Kaiterankatu 1, 90570 Oulu, Finland  \nFull list of author information is available at the end of the article  \nAbstract  \nPiecewise linearization techniques require dividing the signal into multiple pieces each linearized individually. Machine learning (ML) is one of the useful tools to perform the automatic division of these pieces. Complexity reduction in the classification of piecewise digital predistortion is possible through carefully constructing features from both the signal statistics and the power amplifier (PA) characteristics. Our paper introduces two low-complex classical ML-based methods that facilitate the classification of baseband input data into distinct segments. These methods effectively linearize PA behavior by employing tailored Volterra models corresponding to each segment. Moreover, we perform an in-depth analysis of the proposed schemes to further optimize their classification and regression complexities. The two proposed low-complexity approaches are validated by laboratory experiments and show up to 4 dB error vector magnitude (EVM) improvement over the conventional approach for a class A PA at 28 GHz. Similarly, the EVM improvement is up to 2 dB over the vector-switched general memory polynomial scheme. With only one indirect learning architecture iteration, the two proposed schemes obey the 5G new radio standard up to 6.5 dB and 7 dB output backoff, respectively.  \nKeywords: 5G new radio (NR), Behavioral modeling, Low complex, Digital predistortion (DPD), Linearization, Machine learning, Millimeter wave (mmW), Power amplifier (PA)  \n1 Introduction  \nFuture estimates of fifth-generation (5G) new radio (NR) energy consumption by the International Telecommunication Union is around 130% of fourth generation (4G)  \n[1] . Thus, to enable green communications, the millimeter wave (mmW) devices, and sixth-generation (6G) systems require operating the power amplifiers (PAs) at a minimum backoff from saturation power to improve radio frequency (RF) coverage and economize the power consumption. However, this rule degrades the transmitted signal quality due to the nonlinear and memory effects of the PA [2] . To mitigate the caused distortion, digital predistortion (DPD) is widely used to linearize transmitters.  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://](http://)[ ](http://)[creativecommons.org/licenses/by/4.0/](creativecommons.org/licenses/by/4.0/.)[.](creativecommons.org/licenses/by/4.0/.)  \nBulusu etal. EURASIP Journal on Advances in Signal Processing (2024) 2024:97 Page 2 of 24  \nHowever, using DPD requ","cbCaicZPC1A62o26","https://ap.wps.com/l/cbCaicZPC1A62o26","pdf",3669137,3,1,24,"English","en",105,"# Abstract\n# 1 Introduction\n## Motivation: green communications and PA nonlinearity\n## DPD and Volterra-based behavioral modeling\n## Classical ML and piecewise DPD background\n# Proposed PW-DPD approach\n## ML classification + PW Volterra regression","[{\"question\":\"Why does piecewise digital predistortion require dividing the signal into segments?\",\"answer\":\"Piecewise linearization splits the signal into multiple pieces so each piece can be linearized individually, improving modeling of nonlinearities and memory effects.\"},{\"question\":\"What role does machine learning play in the proposed piecewise DPD scheme?\",\"answer\":\"Machine learning performs automatic classification to partition the baseband input into distinct segments, after which tailored Volterra models are applied per segment.\"},{\"question\":\"What experimental improvements are reported compared with conventional approaches?\",\"answer\":\"Laboratory experiments show up to 4 dB EVM improvement over the conventional approach for a class A PA at 28 GHz, and up to 2 dB improvement over the vector-switched generalized memory polynomial scheme.\"}]","Machine learning-based methods for piecewise digital predistortion in mmW 5G NR systems - Research Open Access | PDF",1785942940,60,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"machine-learning-based-methods-for-piecewise-digital-predistortion-in-mmw-5g-nr-systems-research-open-access","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/machine-learning-based-methods-for-piecewise-digital-predistortion-in-mmw-5g-nr-systems-research-open-access/127918/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does piecewise digital predistortion require dividing the signal into segments?","Question",{"text":76,"@type":77},"Piecewise linearization splits the signal into multiple pieces so each piece can be linearized individually, improving modeling of nonlinearities and memory effects.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What role does machine learning play in the proposed piecewise DPD scheme?",{"text":81,"@type":77},"Machine learning performs automatic classification to partition the baseband input into distinct segments, after which tailored Volterra models are applied per segment.",{"name":83,"@type":74,"acceptedAnswer":84},"What experimental improvements are reported compared with conventional approaches?",{"text":85,"@type":77},"Laboratory experiments show up to 4 dB EVM improvement over the conventional approach for a class A PA at 28 GHz, and up to 2 dB improvement over the vector-switched generalized memory polynomial scheme.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":30,"slug":109},5,"Comic","comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]