[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83216-en":3,"doc-seo-83216-105":30,"detail-sidebar-cat-0-en-105":95},{"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},83216,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Design and Deployment Guidelines for UAV-Mounted RIS Under Position Uncertainty","UAV-mounted reconfigurable intelligent surfaces (RIS) support 6G by dynamically shaping wireless propagation, enabling coverage enhancement, integrated sensing and communication, and localization. UAV mobility can preserve line-of-sight links, yet positioning uncertainty causes channel distortions that reduce RIS phase alignment and coherent combining. A GUM-based uncertainty propagation framework maps UAV position uncertainty through the geometric Tx–RIS–Rx model into the complex cascaded channel. Closed-form stochastic modeling shows exponential coherence loss dominated by phase uncertainty, and introduces a performance-driven coherence threshold to guide design and placement.","Design and Deployment Guidelines for UAV-Mounted RIS Under Position Uncertainty  \nKevin Weinberger†, David M¨uller∗ , Martin Mnnigmann∗ , Aydin Sezgin†,  \n†Institute of Digital Communication Systems, Ruhr-Universitt Bochum, Germany,∗Automatic Control and Systems Theory, Ruhr-Universitt Bochum, Germany,{Kevin.Weinberger, David.Mueller-r21, Martin.Moennigmann, [Aydin.Sezgin](Aydin.Sezgin}@rub.de)[}](Aydin.Sezgin}@rub.de)[@rub.de](Aydin.Sezgin}@rub.de)  \nAbstract—UAV-mounted reconfigurable intelligent surfaces (RIS) are a promising enabler for 6G networks, offering dynamic control of wireless propagation for coverage enhancement, integrated sensing and communication (ISAC), and localization.  \nJul 2026  \nBy exploiting UAV mobility, RIS can maintain favorable line-ofsight links, improving channel quality in dynamic environments. However, UAV positioning uncertainties introduce channel distortions that degrade RIS phase alignment and coherent combining. This work develops a GUM-based uncertainty propagation framework for UAV-mounted RIS channels, mapping UAV position uncertainty through the geometric Tx–RIS–Rx model into the complex cascaded channel. We derive a closed-form stochastic propagation model capturing nonlinear phase uncertainty effects and quantify their impact on channel coherence. The results show that phase uncertainty induces exponential coherence loss, dominating performance degradation. To characterize this transition, we introduce a performance-driven coherence threshold (PCT) that defines the boundary where incoherent combining results in a predetermined performance loss. Results based on  \nFigure 1: Customized Holybro X500 featuring a mounted RIS prototype, controlled by a Raspberry Pi 4B, and Herelink 1.1 controller.  \nHowever, mounting an RIS on a UAV exposes the system to both external and internal sources of uncertainty, such as wind disturbances and sensor noise, which result in orientation [7] and position errors [8] . These directly affect Tx–RIS–Rx alignment and degrade phase-coherent combining, since RIS operation relies on precise phase control over large apertures. In contrast, most existing works assume perfect UAV positioning or adopt simplified additive uncertainty models, failing to capture the nonlinear impact of localization errors on RIS coherence. This leaves a gap in physically grounded models linking localization errors to coherent gain degradation.  \nTo address this, we develop a GUM-based uncertainty propagation framework for UAV-mounted RIS channels, mapping UAV position uncertainty through the geometric Tx–RIS–Rx model into the complex cascaded channel. We derive a closedform stochastic model capturing nonlinear phase uncertainty effects. As it turns out, amplitude uncertainty leads to mild additive power variations, whereas phase uncertainty induces exponential coherence loss that dominates large-scale RIS performance.  \nMotivated by this insight, we introduce a performance-driven coherence threshold (PCT) that characterizes the transition between coherent and incoherent phase combining as a function of RIS size, frequency, and uncertainty level, providing a compact design metric for RIS operation under uncertainty. Finally, analytical scaling laws and measurement-informed Monte Carlo simulations validate the proposed PCT and reveal that optimal UAV-mounted RIS placement is jointly governed by geometry and uncertainty, yielding unintuitive design implications.  \nII. SYSTEM MODEL  \nThe UAV carrying the RIS (depicted in Fig. 1) is assumed to hover in the air while carrying the reflecting surface, which is  \noriented toward the ground. A single-antenna transmitter (Tx) sends signals to the RIS prototype [9], which consists of M = 120 reflecting elements. The reflected signals are then received by a single-antenna receiver (Rx) . By applying controllable phase shifts to the reflected waves, the resulting effective TxRIS-Rx channel is expressed as  \nM  \nheff = pGT  pGR X hm ϕmgm , (1)  \nm=1","cbCaiuRbc45oSdgZ","https://ap.wps.com/l/cbCaiuRbc45oSdgZ","pdf",4333989,3,1,6,"English","en",105,"# Abstract\n# System Model\n## UAV-mounted RIS setup\n## Cascaded Tx–RIS–Rx channel model\n# Uncertainty of the UAV’s Position\n## Measurement Data Acquisition\n## Uncertainty Quantification","[{\"question\":\"为什么UAV安装的RIS在实际部署中会出现性能退化？\",\"answer\":\"UAV位置误差与姿态扰动会破坏Tx–RIS–Rx对准，导致RIS相位对齐与相干叠加变差，从而降低性能。\"},{\"question\":\"GUM不确定度传播框架具体做了什么？\",\"answer\":\"将UAV位置的不确定性通过几何Tx–RIS–Rx模型映射到复级联信道中，形成考虑非线性相位不确定性的随机传播模型。\"},{\"question\":\"相位不确定性和幅度不确定性对信道相干性的影响有何差异？\",\"answer\":\"幅度不确定性主要带来较温和的功率扰动；而相位不确定性会引起指数级的相干损失，并成为大规模RIS性能下降的主导因素。\"},{\"question\":\"性能驱动相干阈值（PCT）用来解决什么问题？\",\"answer\":\"PCT刻画相干到非相干相位叠加的转变边界，作为紧凑的设计指标，帮助在给定RIS尺寸、频率与不确定度条件下进行RIS运行与放置决策。\"}]",1784186000,15,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"design-and-deployment-guidelines-for-uav-mounted-ris-under-position-uncertainty","",{"@graph":36,"@context":89},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"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":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/design-and-deployment-guidelines-for-uav-mounted-ris-under-position-uncertainty/83216/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"为什么UAV安装的RIS在实际部署中会出现性能退化？","Question",{"text":75,"@type":76},"UAV位置误差与姿态扰动会破坏Tx–RIS–Rx对准，导致RIS相位对齐与相干叠加变差，从而降低性能。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"GUM不确定度传播框架具体做了什么？",{"text":80,"@type":76},"将UAV位置的不确定性通过几何Tx–RIS–Rx模型映射到复级联信道中，形成考虑非线性相位不确定性的随机传播模型。",{"name":82,"@type":73,"acceptedAnswer":83},"相位不确定性和幅度不确定性对信道相干性的影响有何差异？",{"text":84,"@type":76},"幅度不确定性主要带来较温和的功率扰动；而相位不确定性会引起指数级的相干损失，并成为大规模RIS性能下降的主导因素。",{"name":86,"@type":73,"acceptedAnswer":87},"性能驱动相干阈值（PCT）用来解决什么问题？",{"text":88,"@type":76},"PCT刻画相干到非相干相位叠加的转变边界，作为紧凑的设计指标，帮助在给定RIS尺寸、频率与不确定度条件下进行RIS运行与放置决策。","https://schema.org",{"og:url":51,"og:type":91,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":93,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":96},[97,101,105,109,114,118,123,126,131,134,138],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},"Technology",50,"technology",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":124,"slug":125},30,"research-report",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":46,"category_name":140,"show_sort_weight":110,"slug":141},19,"General","general"]