[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81744-en":3,"doc-seo-81744-105":30,"detail-sidebar-cat-0-en-105":96},{"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},81744,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Active Sensing for RIS-Aided Tracking and Power Control A Hybrid Neuroevolution and Supervised Learning Approach","Energy-efficient tracking of power-limited mobile users is studied using assistance from a Reconfigurable Intelligent Surface (RIS). Because pilot transmissions dominate the energy budget, a low-overhead BS-to-user feedback link is introduced to enable dynamic uplink power control. A Dual-Agent (DA) deep learning framework jointly optimizes discrete RIS phase profiles and UE transmit power in real time, combining neuroevolution training with supervised learning. The method supports single- and multi-antenna BSs and yields robust tracking and localization gains over EKF, particle filters, machine learning trackers, fingerprinting, reinforcement learning baselines, and standard backpropagation estimators.","Active Sensing for RIS-Aided Tracking and Power Control: A Hybrid Neuroevolution and Supervised Learning Approach  \nGeorge Stamatelis, Student Member, IEEE, Hui Chen, Member, IEEE, Henk Wymeersch, Fellow, IEEE, and George C. Alexandropoulos, Senior Member, IEEE  \narXiv :2607 .00056v2 [ cs .IT] 2 Jul 2026  \nAbstract—This paper studies energy efficient tracking of power-limited mobile users with the assistance of a Reconfigurable Intelligent Surface (RIS). Since localization pilot transmissions dominate the energy budget of power-constrained devices, we introduce a low-overhead feedback link from the Base Station (BS) to the user to enable dynamic uplink power control. To navigate the discrete and decentralized nature of this active sensing problem, we propose a novel Dual-Agent (DA) deep learning framework that jointly optimizes the discrete RIS phase profiles and the UE’s transmit power in real time. Specifically, our approach employs a hybrid training methodology integrating the neuroevolution paradigm with supervised learning, effectively overcoming the non-differentiability of discrete phase responses from the RIS unit elements and the strict information bottleneck of single-bit feedback messages for pilot power control. The proposed DA active sensing framework can be applied with both single- and multi-antenna BSs, the latter with only minor modifications in the structure of one NN: an additional output branch with appropriate structure is included for the latter case to select a valid digital combiner from a finite set. Extensive numerical simulations demonstrate that the proposed scheme achieves highly accurate and robust tracking across diverse target motion models, outperforming extended Kalman and particle filters, as well as, machine learning-based trackers. Furthermore, in static localization, it is shown to significantly outperform traditional fingerprinting schemes, deep reinforcement learning baselines, and standard backpropagation-based estimators.  \nIndex Terms—Tracking, beamforming, power control, multiagent systems, neuroevolution, reconfigurable intelligent surface.  \nI. INTRODUCTION  \nPrecise device and object location knowledge can significantly enhance modern wireless applications, including robotic navigation [2], intelligent vehicles and traffic management [3], [4], Internet of Things (IoT) [5], [6], as well as assisted living [7] . However, accurate location estimation in multipath environments is a challenging problem, especially when trying to localize power-limited lightweight IoT devices. To this end,  \nA preliminary version of this manuscript has been presented at the IEEE ICASSP, Barcelona, Spain, May 2026 [1] .  \nG. Stamatelis and G. C. Alexandropoulos are with the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis Ilissia, 16122 Athens, Greece (e-mails:{georgestamat, [alexandg](alexandg}@di.uoa.gr)[}](alexandg}@di.uoa.gr)[@di.uoa.gr](alexandg}@di.uoa.gr)).  \nH. Chen and H. Wymeersch are with the Department of Electrical Engineering, Chalmers University of Technology, 412 58 Gothenburg, Sweden (e-mails: {hui.chen, [henkw](henkw}@chalmers.se)[}](henkw}@chalmers.se)[@chalmers.se](henkw}@chalmers.se)).  \nThis work has been supported by the Smart Networks and Services Joint Undertaking project 6G-DISAC under the European Union’s Horizon Europe research and innovation programme under Grant Agreement No 101139130 .  \nG. Stamatelis was also supported by the Hellenic Foundation for Research and Innovation (HFRI) under the 5th Call for HFRI PhD Fellowships (Fellowship Number: 21080) .  \nReconfigurable Intelligent Surfaces (RISs) [8] have emerged asa powerful candidate for enhancing the performance of various communication, localization, and sensing schemes [9]–[14] .  \nAn RIS consists of a large number of low-cost, passive elements that can intelligently manipulate electromagnetic waves, typically radio waves, to enhance the performance of ","cbCaikCDRq7Md63q","https://ap.wps.com/l/cbCaikCDRq7Md63q","pdf",1655723,5,1,15,"English","en",105,"# I. INTRODUCTION\n## A. Background and Related Works\n### a) Active Sensing and Localization","[{\"question\":\"What problem does the paper address?\",\"answer\":\"It addresses energy-efficient tracking of power-limited mobile users by leveraging a Reconfigurable Intelligent Surface (RIS) while controlling uplink transmit power under feedback constraints.\"},{\"question\":\"How does the proposed method decide RIS configurations and transmit power?\",\"answer\":\"It uses a Dual-Agent deep learning framework that jointly optimizes discrete RIS phase profiles and the user’s transmit power in real time.\"},{\"question\":\"Why is a hybrid neuroevolution and supervised learning training strategy used?\",\"answer\":\"Because RIS phase responses are discrete and non-differentiable, and because the feedback available for pilot power control is limited to single-bit messages, making purely supervised gradient-based training insufficient.\"},{\"question\":\"What performance improvements are reported?\",\"answer\":\"Numerical simulations show accurate and robust tracking across diverse motion models, outperforming EKF, particle filters, and machine learning trackers; for static localization, it significantly surpasses fingerprinting, deep reinforcement learning baselines, and standard backpropagation estimators.\"}]",1784175792,38,{"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":91,"head_meta":93,"extra_data":95,"updated_unix":28},"active-sensing-for-ris-aided-tracking-and-power-control-a-hybrid-neuroevolution-and-supervised-learning-approach","",{"@graph":36,"@context":90},[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/active-sensing-for-ris-aided-tracking-and-power-control-a-hybrid-neuroevolution-and-supervised-learning-approach/81744/",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","2026-07-24","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82,86],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address?","Question",{"text":76,"@type":77},"It addresses energy-efficient tracking of power-limited mobile users by leveraging a Reconfigurable Intelligent Surface (RIS) while controlling uplink transmit power under feedback constraints.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method decide RIS configurations and transmit power?",{"text":81,"@type":77},"It uses a Dual-Agent deep learning framework that jointly optimizes discrete RIS phase profiles and the user’s transmit power in real time.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is a hybrid neuroevolution and supervised learning training strategy used?",{"text":85,"@type":77},"Because RIS phase responses are discrete and non-differentiable, and because the feedback available for pilot power control is limited to single-bit messages, making purely supervised gradient-based training insufficient.",{"name":87,"@type":74,"acceptedAnswer":88},"What performance improvements are reported?",{"text":89,"@type":77},"Numerical simulations show accurate and robust tracking across diverse motion models, outperforming EKF, particle filters, and machine learning trackers; for static localization, it significantly surpasses fingerprinting, deep reinforcement learning baselines, and standard backpropagation estimators.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":97},[98,102,106,110,114,119,124,127,132,135,139],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":20,"slug":142},19,"General","general"]