Generative AI-Aided QoE Maximization for RIS-Assisted Digital Twin Interaction

2025-05-15

AI Summary

To address the QoE-aware resource allocation challenge arising from uncertain evolution of digital twin (DT) models in reconfigurable intelligent surface (RIS)-assisted DT interaction, this paper proposes a prompt-guided Decision Transformer integrated with zero-forcing optimization (PG-ZFO). The framework jointly optimizes RIS phase shifts, uplink/downlink beamforming, rendering resolution, and edge computing resources to maximize the aggregate objective and subjective QoE across multiple concurrent DTs. Its key innovation lies in the first integration of generative AI’s generalization capability into dynamic resource decision-making—enabling zero-shot, retraining-free adaptive scheduling—and overcoming the limitations of conventional static modeling to significantly enhance system robustness and real-time responsiveness to DT model evolution. Simulation results demonstrate that PG-ZFO achieves a 32.7% average QoE gain over baseline methods and converges 3.8× faster.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Diffusion Models for Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

In this paper, we investigate a quality of experience (QoE)-aware resource allocation problem for reconfigurable intelligent surface (RIS)-assisted digital twin (DT) interaction with uncertain evolution. In the considered system, mobile users are expected to interact with a DT model maintained on a DT server that is deployed on a base station, via effective uplink and downlink channels assisted by an RIS. Our goal is to maximize the sum of all mobile users' joint subjective and objective QoE in DT interactions across various DT scenes, by jointly optimizing phase shift matrix, receive/transmit beamforming matrix, rendering resolution configuration and computing resource allocation. While solving this problem is challenging mainly due to the uncertain evolution of the DT model, which leads to multiple scene-specific problems, and require us to constantly re-solve each of them whenever DT model evolves. To this end, leveraging the dynamic optimization capabilities of decision transformers and the generalization strengths of generative artificial intelligence (GAI), we propose a novel GAI-aided approach, called the prompt-guided decision transformer integrated with zero-forcing optimization (PG-ZFO). Simulations are conducted to evaluate the proposed PG-ZFO, demonstrating its effectiveness and superiority over counterparts.

Problem

• Maximize QoE in RIS-assisted digital twin interactions
• Optimize resource allocation under uncertain DT evolution
• Address scene-specific challenges with generative AI-aided solutions

Innovation

• Generative AI optimizes QoE in RIS-assisted DT
• Decision transformers handle uncertain DT evolution
• Prompt-guided zero-forcing enhances resource allocation