[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85507-en":3,"doc-seo-85507-105":29,"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":20,"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":13,"seo_description":14,"update_tm":27,"read_time":28},85507,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control","Future 6G networks must support low-altitude wireless networks (LAWNs) where UAVs and aerial robots operate in highly dynamic 3D environments under strict latency, reliability, and autonomy constraints. Autonomous edge task execution requires holistic coordination among sensing, communication, computing, and control (SC3). Agentic AI-RAN turns the edge into an autonomous decision-making entity for resource-limited agents. This work proposes a task-oriented Agentic AIRAN architecture executing SC3 within a single edge node and validates it via a GPU prototype and autonomous drone navigation, achieving low closed-loop latency and robust bidirectional communication.","Agentic AI-RAN Empowering Synergetic Sensing, Communication, Computing, and Control  \nLingxiao Sun, Zhaoyang Zhang, Zihan Lin, Zirui Chen, Weijie Zhou, Zhaohui Yang, and Tony Q. S. Quek  \narXiv :2601 . 16565v2 [ ee ss . SY] 13 Jul 2026  \nAbstract—Future sixth-generation (6G) networks are expected to support low-altitude wireless networks (LAWNs), where unmanned aerial vehicles (UAVs) and aerial robots operate in highly dynamic three-dimensional environments under stringent latency, reliability, and autonomy requirements. In such scenarios, autonomous task execution at the network edge demands holistic coordination among sensing, communication, computing, and control (SC3) processes. Agentic Artificially Intelligent Radio Access Networks (Agentic AI-RAN) offer a promising paradigm by enabling the edge network to function as an autonomous decisionmaking entity for low-altitude agents with limited onboard resources. In this article, we propose a task-oriented Agentic AIRAN architecture that enables SC3 task execution within a single edge node. The proposed architecture addresses the challenge of coordinating heterogeneous workloads in resource-constrained edge environments. To validate this framework, we prototype a representative low-altitude UAV system on a general-purpose Graphics Processing Unit (GPU) platform and evaluate it through an autonomous drone-navigation case study. The current prototype instantiates the platform-agnostic design through MultiInstance GPU (MIG) partitioning and containerized deployment, providing physical resource isolation and coordinated execution between real-time communication and multimodal inference. Experimental results demonstrate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, highlighting the feasibility of the proposed framework for mission-critical low-altitude wireless networks in 6G.  \nIndex Terms—Low-Altitude Wireless Networks, Agentic AIRAN, Edge Intelligence, SC3 Task Execution.  \nI. INTRODUCTION  \nThe sixth-generation (6G) wireless networks are envisioned to deliver not only ultra-high-speed connectivity but also deterministic performance to support autonomous, real-time operations. This evolution is particularly critical for emerging low-altitude wireless networks, where unmanned aerial vehicles (UAVs) and aerial robots must operate in complex, threedimensional environments under stringent latency, reliability, and autonomy constraints [1] . Unlike terrestrial devices, lowaltitude platforms face severe Size, Weight, and Power (SWaP) limitations that restrict onboard computing capabilities. As a result, they can neither rely solely on local processing for complex missions nor depend on centralized cloud control due to unstable air-to-ground links. These constraints motivate a  \nLingxiao Sun, Zhaoyang Zhang (corresponding author), Zihan Lin, Zirui Chen, Weijie Zhou, and Zhaohui Yang are with College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China, and also with Zhejiang Provincial Key Laboratory of Multi-modal Communication Networks and Intelligent Signal Processing, Hangzhou 310027, China.  \nT. Q. S. Quek is with the ISTD Pillar, Singapore University of Technology and Design (SUTD), Singapore 487372, and also with the SUTD-ZJU IDEA Center of Network Intelligence, Singapore 487372 .  \nshift toward edge-enabled autonomous control, in which the network edge functions as an external decision logic entity located close to data sources and actuators. Such an external brain must simultaneously process high-bandwidth sensory data and sustain reliable control links, placing significant and heterogeneous demands on edge computing resources.  \nRecent advances in agentic artificial intelligence (Agentic AI) provide a promising foundation for addressing this challenge. This paradigm reconceptualizes edge nodes not merely as data relays, but as intelligent agents capable of autonom","cbCaip8VfFk50VXP","https://ap.wps.com/l/cbCaip8VfFk50VXP","pdf",2127638,1,7,"English","en",105,"# Introduction\n## Motivation for edge-enabled autonomy\n## Agentic AI-RAN as an SC3 orchestration framework\n## Paradigm conflict in existing RAN architectures","[{\"question\":\"What problem does Agentic AI-RAN address in 6G low-altitude wireless networks?\",\"answer\":\"It targets the need for autonomous edge-side task execution in LAWNs, where UAVs and aerial robots require strict latency, reliability, and autonomy. It coordinates sensing, communication, computing, and control (SC3) as a single closed-loop task.\"},{\"question\":\"How does the proposed architecture execute SC3 tasks within an edge node?\",\"answer\":\"It uses a task-oriented Agentic AIRAN architecture designed to coordinate heterogeneous SC3 workloads in resource-constrained edge environments. The implementation supports platform-agnostic execution through Multi-Instance GPU (MIG) partitioning and containerized deployment.\"},{\"question\":\"What evidence supports feasibility of the framework?\",\"answer\":\"A GPU-based prototype and an autonomous drone-navigation case study evaluate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, indicating suitability for mission-critical low-altitude wireless networks in 6G.\"}]",1784204063,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"agentic-ai-ran-empowering-synergetic-sensing-communication-computing-and-control","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/agentic-ai-ran-empowering-synergetic-sensing-communication-computing-and-control/85507/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does Agentic AI-RAN address in 6G low-altitude wireless networks?","Question",{"text":75,"@type":76},"It targets the need for autonomous edge-side task execution in LAWNs, where UAVs and aerial robots require strict latency, reliability, and autonomy. It coordinates sensing, communication, computing, and control (SC3) as a single closed-loop task.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed architecture execute SC3 tasks within an edge node?",{"text":80,"@type":76},"It uses a task-oriented Agentic AIRAN architecture designed to coordinate heterogeneous SC3 workloads in resource-constrained edge environments. The implementation supports platform-agnostic execution through Multi-Instance GPU (MIG) partitioning and containerized deployment.",{"name":82,"@type":73,"acceptedAnswer":83},"What evidence supports feasibility of the framework?",{"text":84,"@type":76},"A GPU-based prototype and an autonomous drone-navigation case study evaluate low closed-loop latency, robust bidirectional communication, and stable performance under dynamic runtime conditions, indicating suitability for mission-critical low-altitude wireless networks in 6G.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]