[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-502998-105":59,"doc-detail-502998-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","configuration-tuning-for-isac-cost-efficient-adaptation-via-race-cma","Configuration Tuning for ISAC - Cost-Efficient Adaptation via RACE-CMA","","This paper studies a feedback-driven configuration-tuning framework for adaptive sensing feedback in Integrated Sensing and Communication (ISAC) systems. A user equipment (UE) adapts sensing parameters under dynamic conditions while satisfying network-defined constraints. The task is cast as a stochastic constrained optimization problem aimed at improving sensing reliability and latency. The work focuses on an abistatic ISAC sensing-feedback setup and applies threshold optimization as a case study, enabling benchmarking. To meet UE compute limits, it proposes RACE-CMA, a ranking-aware constrained and efficient CMA-ES method using two-stage racing, common random numbers, noise-aware ranking, and feasible-constraint handling. Results show ~35% higher reliability with ~25% less computation, yielding roughly twofold performance-cost efficiency.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/configuration-tuning-for-isac-cost-efficient-adaptation-via-race-cma/502998/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/configuration-tuning-for-isac-cost-efficient-adaptation-via-race-cma/502998.png","ImageObject",300,407,{"name":92,"@type":93},"pixelkiddo","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-19","2026-10-03",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main problem the paper addresses in ISAC systems?","Question",{"text":112,"@type":113},"It addresses how to adapt UE-side sensing configuration parameters online in a closed-loop feedback setting, rather than relying on static, worst-case RRC settings that underperform in dynamic environments.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed framework formulate the tuning task?",{"text":117,"@type":113},"It formulates configuration tuning as a stochastic constrained optimization problem, targeting improved sensing reliability and latency while respecting network-defined constraints.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is RACE-CMA introduced, and what techniques does it use?",{"text":121,"@type":113},"RACE-CMA is designed to be noise-robust and cost-efficient under UE computational limits. It integrates two-stage racing, common random numbers, noise-aware ranking, and feasible constraint handling.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},502998,1791294226,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962090883892,"https://ap-avatar.wpscdn.com/avatar/e00115db34f3e0f11b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1790152879550218658","Configuration Tuning for ISAC: Cost-Efficient Adaptation via RACE-CMA  \nAshkan Jafari Fesharaki∗ , Yasser Mestrah†, Ibrahim Hemadeh†, Yi Ma∗ , Mohammad Heggo †, Arman Shojaeifard†, Ahmet Serdar Tan†, Rahim Tafazolli∗ , and Alain Mourad†  \n∗ 6GIC, Institute for Communication Systems, University of Surrey, Guildford, UK, GU2 7XH  \n† InterDigital, London, UK, EC2A 3QR  \narXiv :2604 .05792v1 [ ee ss . SP] 7 Apr 2026  \nAbstract—This paper studies a feedback-driven configuration-tuning framework for adaptive sensing feedback in Integrated Sensing and Communication (ISAC) systems. We propose a framework in which the User Equipment (UE) adapts sensing parameters under dynamic conditions while satisfying network-defined constraints. The problem is formulated as a stochastic constrained optimization problem, to improve sensing reliability and latency. We consider abistatic ISAC sensing-feedback setup and instantiate the framework via threshold optimization as a representative case study, enabling benchmarking against baseline methods. To ensure efficiency under UE computational limits, we propose Ranking-Aware, Constrained, and Efficient CMA-ES (RACE-CMA), which integrates two-stage racing, common random numbers, noise-aware ranking, and feasible constraint handling. Results show that the proposed approach improves sensing reliability by about 35% while reducing computational cost by about 25%, yielding roughly a twofold gain in performance–cost efficiency. This highlights that UE-side configuration tuning is a promising mechanism for enhancing closed-loop ISAC performance under practical system constraints.  \nIndex Terms—ISAC, sensing, closed-loop feedback, configuration tuning, CMA-ES, optimization  \nI. Introduction  \nSixth-generation (6G) wireless networks are envisioned to integrate sensing, communication, and control into a unified infrastructure [1]–[3] . Integrated Sensing and Communication (ISAC) is a key enabler of this vision, transforming the radio access network into a distributed sensory system capable of perceiving and interacting with its surroundings [4], [5] . By jointly exploiting communication waveforms for perception, ISAC enables new capabilities in localization, mobility management, and context-aware networking [6], [7], as reflected in ongoing 3GPP activities toward sensing-enabled networks [8] .  \nIn current 5G and pre-6G systems, User Equipment (UE) behavior is governed by network-defined Radio Resource Control (RRC) configurations, where parameters such as thresholds and timers are fixed for worst-case conditions. This static design is simple but inefficientfor ISAC sensing in dynamic environments, leading to redundant reports, delayed feedback, and frequent RRC reconfigurations [9] . Because sensing performance depends on interference, target mobility, and channel variation, fixed settings cannot ensure reliability or responsiveness [10] .  \nBy dynamically refining reporting thresholds, timers, resource block (RB) density, beam-sweep cadence, and sensing periodicity, the UE can maintain stable sensing Key Performance Indicators (KPIs) without repeated RRC Reconfiguration. This approach reduces signaling overhead, improves sensing accuracy and responsiveness, and optimizes resource usage while staying fully compliant with operator policy. In this work, configuration tuning is the overarching mechanism, and we apply it atop the Smart Sensing Feedback (SSF) mechanism [11] as a concrete use case. In brief, SSF defines how the UE reports target-related information (e.g., detected, lost, null) to the transmitting entity. The tunable configuration vector (e.g., thresholds, timers, sensing periodicities, and beamsweep cadence) is adjusted within network-defined bounds to maintain sensing KPIs under dynamic conditions. For example, thresholds map power measurements (e.g., Reflected Echo Strength Indicator (RESI), Reference Signal Received Power (RSRP)) to UE actions (e.g. , reporting, power scaling, reconfiguration) . If se","cbCaioWIFzQLGVge","https://ap.wps.com/l/cbCaioWIFzQLGVge","pdf",867047,"English","# Introduction\n## Motivation: static RRC configurations in dynamic sensing\n## Proposed approach: UE-side sensing configuration tuning under SSF\n## Optimization challenge and gap in prior work\n# Contributions and framework overview","[{\"question\":\"What is the main problem the paper addresses in ISAC systems?\",\"answer\":\"It addresses how to adapt UE-side sensing configuration parameters online in a closed-loop feedback setting, rather than relying on static, worst-case RRC settings that underperform in dynamic environments.\"},{\"question\":\"How does the proposed framework formulate the tuning task?\",\"answer\":\"It formulates configuration tuning as a stochastic constrained optimization problem, targeting improved sensing reliability and latency while respecting network-defined constraints.\"},{\"question\":\"Why is RACE-CMA introduced, and what techniques does it use?\",\"answer\":\"RACE-CMA is designed to be noise-robust and cost-efficient under UE computational limits. It integrates two-stage racing, common random numbers, noise-aware ranking, and feasible constraint handling.\"}]","Configuration Tuning for ISAC - Cost-Efficient Adaptation via RACE-CMA | PDF",1791047482,18]