Abstract
From AI-assisted art creation to large language model (LLM)-powered ChatGPT, AI-generated contents and services are becoming a transforming force. It calls for the telecom industry to embrace the prospects of AIGC services and face the unique challenges posed by incorporating generative model services into the AI-native 6G wireless network paradigm. We propose enabling AIGC inference services on mobile devices by optimizing MEC-device computing offloading, through which AIGC task latency is minimized by reinforcement learning based policy agent in a computing resource constrained and bandwidth limited wireless environment. Simulation results are presented to demonstrate the performance advantage.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 232-236 |
| Number of pages | 5 |
| Journal | IEEE Networking Letters |
| Volume | 6 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2024 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Information Systems
- Communication
- Hardware and Architecture
Keywords
- 6G
- AIGC
- ChatGPT
- LLM
- MEC
- constrained reinforcement learning
- on-device computing