Diffusion-Based Reinforcement Learning for Edge-Enabled AI-Generated Content Services
Nanyang Technological University · University of Electronic Science and Technology of China · +4 more institutions
Abstract
As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) emerges as a key solution, yet the resource-intensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing…
Citation impact
- FWCI
- 47.95
- Percentile
- 100%
- References
- 55
Authors
7Topics & keywords
- Computer science
- Reinforcement learning
- Service discovery
- Wireless
- Service (business)
- Enhanced Data Rates for GSM Evolution
- Key (lock)
- Artificial intelligence