[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86437-en":3,"doc-seo-86437-105":30,"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":13,"seo_description":14,"update_tm":28,"read_time":29},86437,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting","Radio frequency fingerprint identification (RFFI) uses transmitter-specific hardware imperfections as a physical-layer identity cue for IoT devices, yet deep RFFI models degrade when the acquisition environment changes. In multi-antenna reception, degradation is driven by receiver-array topology, frequency-offset dynamics, and capture-dependent target structure that can distort embeddings and shift source-trained boundaries. This work introduces physics-informed structure anchoring with capture-aware prototype calibration (PISA-CAPC), separating representation anchoring from fixed-backbone target calibration and enabling label-free capture-aware score calibration.","Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting  \nFengchong Yao, Jianbing Li, Qing Liu, Qikun Liu, Kefeng Song, Haitao Li, and Song Wang  \narXiv :2607 .09760v1 [ ee ss . SP] 6 Jul 2026  \nAbstract—Radio frequency fingerprint identification (RFFI) uses transmitter-specific hardware imperfections as a physicallayer identity cue for Internet of Things (IoT) devices, but deep RFFI models often degrade when the acquisition environment changes. In multi-antenna reception, this degradation is not merely a generic distribution shift. It is also shaped by receiver-array topology, frequency-offset dynamics, and capturedependent target structure, which can distort embeddings and move source-trained decision boundaries. This article proposes physics-informed structure anchoring with capture-aware prototype calibration (PISA-CAPC), a framework that separates source representation anchoring from fixed-backbone target calibration. The representation stage organizes antenna tokens with a topology graph and modulates the graph using CFO-derived acquisition-dynamics descriptors. Bounded contextual residual suppression is then applied around the identity representation. At deployment, unlabeled capture-aware prototype calibration (U-CAPC) calibrates target decision scores through capturelocal prototype evidence under a fixed representation, mitigating boundary shift without requiring target-domain backbone updates or target labels. On a measured ten-transmitter multiantenna WiFi benchmark, PISA-CAPC achieves 0.9257 targetdomain mean Macro-F1 under a balanced transductive setting. Ablations confirm that topology-guided structure anchoring, contextual residual suppression, and capture-aware calibration contribute complementary gains. These results establish PISACAPC as a fixed-backbone route to cross-environment RFFI, coupling physically motivated representation learning with labelfree, capture-aware decision calibration.  \nIndex Terms—Internet of Things, physical-layer authentication, radio frequency fingerprint identification, antenna topology, structure-anchored representation, contextual residual suppression, prototype calibration.  \nI. INTRODUCTION  \nRELIABLE device authentication remains difficult in open  \nInternet of Things (IoT) deployments. Many low-cost, mobile, or noncooperative devices operate at large scale under limited computational and communication resources. Credential-based mechanisms alone do not fully cover this setting: credentials can be copied, lost, or unavailable to the receiver during passive monitoring. Radio frequency fingerprint identification (RFFI), also known as specific emitter identification (SEI), offers a complementary physical-layer identity cue  \nFengchong Yao, Jianbing Li, Qing Liu, Qikun Liu, Kefeng Song, Haitao Li, and Song Wang are with the School of Information Systems Engineering, Information Engineering University, Zhengzhou 450001, China.  \nE-mail: Fengchong Yao ([phoenixly@126.com](phoenixly@126.com)), Jianbing Li ([li_jb@126.com](li_jb@126.com)), Qing Liu ([liuqing8123@163.com](liuqing8123@163.com)), Qikun Liu  \n([ed-liuqikun@163.com](ed-liuqikun@163.com)), Kefeng Song ([annx1990@163.com](annx1990@163.com)), Haitao  \nLi ([lihaitao_01@163.com](lihaitao_01@163.com)), Song Wang ([wangsong8190@163.com](wangsong8190@163.com)).  \nCorresponding author: Jianbing Li (e-mail: [li_jb@126.com](li_jb@126.com)).  \nby exploiting small and uncontrollable hardware differences caused by manufacturing tolerances, oscillator imperfections, power-amplifier nonlinearities, and other transmitter-side impairments [1],[2] . Since these fingerprints are embedded in the emitted waveform, RFFI can provide device identity evidence without modifying the upper-layer communication stack.  \nRecent deep-learning-based RFFI methods have shown strong recognition ability when training and testing samples are acquired under comparable conditions. Convolutional network","cbCaihhIkniHzroc","https://ap.wps.com/l/cbCaihhIkniHzroc","pdf",1951549,5,1,17,"English","en",105,"# Abstract\n# Introduction\n## Motivation: cross-environment degradation in RFFI\n## Problem framing: representation-level and decision-level mismatch\n# Proposed Framework (PISA-CAPC)\n## Representation stage with topology graph and CFO descriptors\n## Contextual residual suppression around identity representations\n## Deployment stage with unlabeled capture-aware prototype calibration (U-CAPC)\n# Experimental Results\n## Multi-antenna WiFi benchmark performance and transductive setting\n## Ablation study for complementary gains","[{\"question\":\"Why do deep RFFI models degrade under environment changes?\",\"answer\":\"Because receiver-array topology, frequency-offset dynamics, and capture-dependent target structure distort the learned embeddings and can shift decision boundaries, making source-domain-trained features unreliable in new acquisition contexts.\"},{\"question\":\"What does PISA-CAPC do differently from fixed-label or purely statistical adaptation?\",\"answer\":\"It anchors the source representation using physics-informed structure (topology graph and CFO-derived descriptors), then applies capture-aware prototype calibration at deployment with a fixed backbone to mitigate boundary shift without target-domain backbone updates or target labels.\"},{\"question\":\"How is unlabeled capture-aware prototype calibration (U-CAPC) applied during deployment?\",\"answer\":\"U-CAPC calibrates target decision scores using capture-local prototype evidence under a fixed representation, reducing boundary shift while avoiding reliance on target labels or backbone retraining.\"}]",1784211740,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"physics-informed-structure-anchoring-with-capture-aware-prototype-calibration-for-cross-environment-rf-fingerprinting","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/physics-informed-structure-anchoring-with-capture-aware-prototype-calibration-for-cross-environment-rf-fingerprinting/86437/",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":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",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 do deep RFFI models degrade under environment changes?","Question",{"text":76,"@type":77},"Because receiver-array topology, frequency-offset dynamics, and capture-dependent target structure distort the learned embeddings and can shift decision boundaries, making source-domain-trained features unreliable in new acquisition contexts.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What does PISA-CAPC do differently from fixed-label or purely statistical adaptation?",{"text":81,"@type":77},"It anchors the source representation using physics-informed structure (topology graph and CFO-derived descriptors), then applies capture-aware prototype calibration at deployment with a fixed backbone to mitigate boundary shift without target-domain backbone updates or target labels.",{"name":83,"@type":74,"acceptedAnswer":84},"How is unlabeled capture-aware prototype calibration (U-CAPC) applied during deployment?",{"text":85,"@type":77},"U-CAPC calibrates target decision scores using 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