[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84086-en":3,"doc-seo-84086-105":28,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},84086,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Quality-Aware Personalized AI Service Provisioning in UAV-Assisted 6G Networks","Six-generation (6G) AI services require jointly satisfying conventional communication quality (notably end-to-end latency) and Quality of AI Services (QoAIS), including output fidelity, continuity, and personalization. Existing approaches often focus on latency while overlooking QoAIS for personalized outputs in dynamic aerial-terrestrial settings. This paper proposes HyPE, a hybrid predictive and in-context learning framework for holistic, scalable, QoAIS-aware AI service provisioning with UAVs. HyPE combines mobility-aware spatio-temporal forecasting, LLM-guided trajectory and inference assignment, and heuristic pre-/post-processing service placement and routing.","© 2026 IEEE. Reprinting or republishing this material for the purpose of advertising or promotion, creating new collective works, reselling or redistributing to servers or lists, or using any copyrighted component in other works must adhere to IEEE  \npolicy. The paper has been accepted for publication at IEEE FINE 2026 .  \nQuality-Aware Personalized AI Service Provisioning  \nin UAV-Assisted 6G Networks  \nMohammad Farhoudi 1 , Masoud Shokrnezhad2 , and Tarik Taleb3  \n1 Oulu University, Finland; [mohammad.farhoudi@oulu.fi](mohammad.farhoudi@oulu.fi)  \n2 ICTFICIAL Oy, Espoo, Finland; [masoud.shokrnezhad@ictficial.com](masoud.shokrnezhad@ictficial.com)  \n3 Ruhr University Bochum (RUB), Germany; [tarik.taleb@rub.de](tarik.taleb@rub.de)  \narXiv :2607 .06278v 1 [ cs .NI ] 7 Jul 2026  \nAbstract—In sixth-generation (6G) artificial intelligence (AI) services, two quality dimensions should be jointly addressed: conventional quality (e.g., latency) and Quality of AI Services (QoAIS; output fidelity, continuity, personalization). Existing methods emphasize conventional quality, while neglecting QoAIS, particularly for personalized outputs in dynamic aerial-terrestrial settings. This paper introduces HyPE, a hybrid predictive-incontext-learning framework for holistically quality-aware personalized AI service provisioning in Unmanned Aerial Vehicle (UAV)-assisted 6G networks. HyPE integrates: (i) mobility-aware prediction to forecast spatio-temporal request distributions, (ii) learning-augmented decision leveraging Large Language Model (LLM)-based reasoning to optimize UAV trajectories and inference assignments, and (iii) pre-/post-processing service placement and routing using heuristics. We formulate an optimization problem for joint trajectory planning, service placement, and routing, and present HyPE as a scalable alternative to intractable optimal solutions. Simulations with empirical mobility traces and heterogeneous AI workloads show near-optimal coverage, reduced endto-end latency, sustained QoAIS-driven, and continuity-based service personalization versus optimization and state-of-the-art baselines. The results highlight the promise of predictive learningaugmented provisioning for elastic, user-centric AI in 6G.  \nIndex Terms—Service Provisioning, Quality of AI Services (QoAIS), Edge-Cloud Environment, Intelligent UAV, 6G AerialTerrestrial Networks.  \nI. INTRODUCTION  \nWith the rising demand for Artificial Intelligence (AI) -driven services, sixth-generation (6G) networks should pave the way to accommodate them. 6G is envisioned as an AI-native infrastructure, enabling adaptive service provisioning [1], [2] . It should jointly satisfy two service quality dimensions: conventional quality (End-to-End (E2E) latency) and Quality-of-AI-Services (QoAIS) (output fidelity and continuity-aided user-specific personalization) [3], [4] . Meeting these dual requirements calls for a heterogeneous model stack, where edge-cloud nodes host lightweight distilled generative models for low-latency responses alongside fullscale models for richer reasoning and QoAIS-driven personalization that adapts outputs to user history [5], [6] . To remain scalable under high load, this satisfaction should be modular, leveraging pluggable pre-processing (e.g., filtering, visual encoding) and post-processing (e.g., voice modulation, translation, formatting) stages that can be composed per request to balance E2E latency and QoAIS [7] . Additionally, resource-intensive inference pipelines should be orchestrated  \nin dynamic networks, posing a central challenge for efficient provisioning under variable scenarios [8] .  \nFurthermore, mobility adds complexity to service provisioning, as users expect reliable responses while moving across network regions [9] . 6G networks aim to provide ubiquitous access through AI-native infrastructure and intelligent edge capabilities [10] . To realize such pervasive access, 6G network architectures incorporate aerial platforms as mobile","cbCaiczTCdl9XLqw","https://ap.wps.com/l/cbCaiczTCdl9XLqw","pdf",1011082,1,"English","en",105,"# Introduction\n## Service quality dimensions in 6G AI services\n## UAV-assisted aerial-terrestrial provisioning\n## Prior work and research gaps","[{\"question\":\"What two quality dimensions must 6G AI services address in this work?\",\"answer\":\"The paper distinguishes conventional quality such as end-to-end latency and Quality of AI Services (QoAIS), which includes output fidelity and continuity-driven user-specific personalization.\"},{\"question\":\"What is HyPE and what does it integrate?\",\"answer\":\"HyPE is a hybrid predictive-in-context-learning framework. It integrates mobility-aware prediction for spatio-temporal request forecasting, LLM-based reasoning for decision-making in trajectory and inference assignments, and heuristic-based pre-/post-processing placement and routing.\"},{\"question\":\"How does the paper evaluate the proposed approach?\",\"answer\":\"Simulations use empirical mobility traces and heterogeneous AI workloads, showing near-optimal coverage, reduced end-to-end latency, and sustained QoAIS-driven continuity-based personalization compared with optimization and state-of-the-art baselines.\"}]",1784192630,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":26},"quality-aware-personalized-ai-service-provisioning-in-uav-assisted-6g-networks","",{"@graph":34,"@context":84},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/quality-aware-personalized-ai-service-provisioning-in-uav-assisted-6g-networks/84086/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What two quality dimensions must 6G AI services address in this work?","Question",{"text":74,"@type":75},"The paper distinguishes conventional quality such as end-to-end latency and Quality of AI Services (QoAIS), which includes output fidelity and continuity-driven user-specific personalization.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What is HyPE and what does it integrate?",{"text":79,"@type":75},"HyPE is a hybrid predictive-in-context-learning framework. It integrates mobility-aware prediction for spatio-temporal request forecasting, LLM-based reasoning for decision-making in trajectory and inference assignments, and heuristic-based pre-/post-processing placement and routing.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the paper evaluate the proposed approach?",{"text":83,"@type":75},"Simulations use empirical mobility traces and heterogeneous AI workloads, showing near-optimal coverage, reduced end-to-end latency, and sustained QoAIS-driven continuity-based personalization compared with optimization and state-of-the-art baselines.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":44,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":45,"doc_module":4,"doc_module_name":44,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":44,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":44,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":44,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":44,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":44,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":44,"category_name":124,"show_sort_weight":27,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":27,"doc_module":4,"doc_module_name":44,"category_name":127,"show_sort_weight":27,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":44,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":44,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]