Quality-Aware Personalized AI Service Provisioning in UAV-Assisted 6G Networks

2026-07-07

AI Summary

This work addresses the critical gap in existing 6G AI service research, which largely overlooks Quality of AI Service (QoAIS), thereby failing to ensure personalized output in dynamic space-air-ground scenarios. To bridge this gap, we propose HyPE, a novel framework that, for the first time, integrates QoAIS into UAV-assisted 6G networks. HyPE synergistically combines mobility-aware prediction, large language model-enhanced decision-making, heuristic service placement and routing, and joint optimization of UAV trajectory planning with task assignment. Experimental results demonstrate that HyPE significantly outperforms state-of-the-art and mainstream baselines in coverage, end-to-end latency, QoAIS preservation, and continuity of personalization, achieving scalable and near-optimal deployment of personalized AI services.

Abstract

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-in-context-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 end-to-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 learning-augmented provisioning for elastic, user-centric AI in 6G.

Problem

• Quality of AI Services
• personalization
• UAV-assisted 6G networks
• service provisioning
• dynamic aerial-terrestrial settings

Innovation

• HyPE
• QoAIS
• UAV-assisted 6G
• LLM-based reasoning
• predictive learning-augmented provisioning