[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123974-en":3,"doc-seo-123974-105":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},123974,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",6,"Technology","Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion","The document presents a machine-learning-aided piece-wise behavioral modeling method for power amplifiers used in digital predistortion. Instead of a single pruned Volterra or generalized memory polynomial, it partitions input/output baseband samples into classes and trains an ML classifier using features from signal statistics and the PA operating point. Separate tailored GMP models then linearize class-wise data, targeting improved performance versus computational complexity. Simulation results demonstrate a better trade-off for PA behavior modeling and linearization.","MACHINE LEARNING-AIDED PIECE-WISE MODELING TECHNIQUE OF POWER AMPLIFIER FOR DIGITAL PREDISTORTION  \nS. S. Krishna Chaitanya Bulusu †, Nuutti Tervo†, Praneeth Susarla‡,  \nMikko J. Sillanpa¨a¨􀀃 , Olli Silve´n‡, Markku Juntti†, andAarno Pa¨rssinen†† Centre for Wireless Communications (CWC),‡ Center for Machine Vision and Signal Analysis (CMVS),  \n􀀃 Department of Mathematical Sciences (DMS), University of Oulu, Oulu, FI-90014, Finland.  \nABSTRACT  \nWe propose a new power ampli􀀂er (PA) behavioral modeling approach, to characterize and compensate for the signal quality degrading effects induced by a PA with a machine learning (ML) aided piece-wise (PW) modeling approach. Instead of using a single pruned Volterra model, we use multiple smallsize pruned Volterra models by classifying the input data into different classes. For that purpose, an ML classi􀀂er model is trained by extracting some crucial features from both the input signal statistics and the PA operating point. The simulation results indicate that our approach contributes to an improved performance/complexity trade-off than a single generalized memory polynomial (GMP) model in terms of PA behavior modeling and linearization.  \nIndex Terms— Behavioral modeling, computational complexity, decision tree, digital predistortion (DPD), linearization, machine learning (ML), power ampli􀀂er (PA) .  \n1. INTRODUCTION  \nAs per green communications obligation, a power ampli􀀂er (PA) must be operated as close as possible to its saturation point [1] . However, that leads to wanted effects arising from PA non-linear (NL) behavior such as gain compression, inband (IB), and out-of-band (OOB) distortions [2] .  \nDigital predistortion (DPD) is a popular PA linearization technique requiring PA behavior modeling [3] . PA is also a NL dynamic system. The Volterra series is computationally expensive but can capture the behavior of PA and memory. It has been theoretically proved that the k-th order pre-inverse of a Volterra system is identical to its post-inverse [4] . This paved way for the usage of pruned Volterra models with reduced complexity to retain most of the modeling capabilities, such as memory polynomial (MP) [5], generalized MP (GMP) [6] and dynamic deviation reduction (DDR) [7] and etc.  \nML techniques for DPD have already gained traction [8– 11] . However, it is dif􀀂cult to model the PA behavior with  \nThis is work was supported in part by the Academy of Finland projects 6Genesis Flagship (grant no 346208) and Pro􀀂5 (HiDyn) (grant no 326291) .  \na single model for the entire range of output power because of varying behavior at different power levels. Thus, piecewise (PW) polynomial-based models have been shown to be quite effective in modeling and linearizing PAs with strong nonlinear effects [12–15] . In [12], a vector switched model has been proposed where the input data samples are classi-􀀂ed by a computationally expensive k-means clustering algorithm based on their envelope. Nevertheless, the basic intent of PW modeling and results shown in [12] are interesting as they paved the way with better hardware-friendly techniques such as [13] and [14] . In [13], a low-computation learning algorithm based on a simple decorrelation rule is used for PW modeling. In [14], authors proposed a PW closed-loop DPD solution using low-complexity gradient-adaptive parameter learning algorithms. Recently, a machine learning (ML) -based scheme was proposed in [15] where the input samples are classi􀀂ed by the decision tree classi􀀂er using features like current magnitude and past samples. The sub-model coef􀀂 -cients are then extracted.  \nOur proposed approach involves two stages: ML classi􀀂cation of the input and output baseband signal samples and piecewise digital predistortion (DPD) . The novelty of this scheme is to model an accurate and low-complex machine learning (ML)-aided classi􀀂er in the 􀀂rst stage involving the input and output baseband signal samples. Here, the boundaries between the classes a","cbCairE3MrBu9EsE","https://ap.wps.com/l/cbCairE3MrBu9EsE","pdf",445652,1,5,"English","en",105,"# Abstract\n# Introduction\n# PA Model and Performance Metrics\n## PA Model","[{\"question\":\"What modeling approach is proposed for the power amplifier in digital predistortion?\",\"answer\":\"It proposes an ML-aided piece-wise behavioral modeling scheme that classifies samples and applies class-specific pruned Volterra/GMP models for linearization.\"},{\"question\":\"How does the machine learning classifier determine class boundaries?\",\"answer\":\"The classifier is trained using features extracted from input signal statistics together with the PA operating point, and class boundaries are computed as a function of both.\"},{\"question\":\"Why use multiple small pruned Volterra/GMP sub-models instead of a single generalized model?\",\"answer\":\"Because PA behavior changes across output power levels, a single generalized model struggles to capture this variation; piece-wise sub-models improve the performance/complexity trade-off.\"}]","Machine Learning-Aided Piece-Wise Modeling Technique of Power Amplifier for Digital Predistortion | PDF",1785819517,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-aided-piece-wise-modeling-technique-of-power-amplifier-for-digital-predistortion","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-aided-piece-wise-modeling-technique-of-power-amplifier-for-digital-predistortion/123974/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What modeling approach is proposed for the power amplifier in digital predistortion?","Question",{"text":75,"@type":76},"It proposes an ML-aided piece-wise behavioral modeling scheme that classifies samples and applies class-specific pruned Volterra/GMP models for linearization.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the machine learning classifier determine class boundaries?",{"text":80,"@type":76},"The classifier is trained using features extracted from input signal statistics together with the PA operating point, and class boundaries are computed as a function of both.",{"name":82,"@type":73,"acceptedAnswer":83},"Why use multiple small pruned Volterra/GMP sub-models instead of a single generalized model?",{"text":84,"@type":76},"Because PA behavior changes across output power levels, a single generalized model struggles to capture this variation; 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