Generative AI-Aided QoE Maximization for RIS-Assisted Digital Twin Interaction
This addresses resource allocation for digital twin interactions in mobile networks, but appears incremental as it combines existing techniques like decision transformers and generative AI for a specific domain.
The paper tackles the problem of maximizing quality of experience (QoE) for mobile users interacting with a digital twin via RIS-assisted channels, by jointly optimizing phase shifts, beamforming, rendering, and computing resources, and demonstrates effectiveness through simulations.
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.