Deploying On-Device AIGC Inference Services in 6G via Optimal MEC-Device Offloading
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.
Authors
- Nirwan Ansari (ORCID: https://orcid.org/0000-0001-8541-3565)
- Changshi Zhou
- Weiqi Liu (ORCID: https://orcid.org/0000-0001-5602-7841)
- Tao Han (ORCID: https://orcid.org/0000-0002-6626-1305)
Institutions
- New Jersey Institute of Technology (US)
- Auburn University at Montgomery (US)
Publication Details
- Journal
- IEEE Networking Letters
- Published
- 2024-11-04
- DOI
- https://doi.org/10.1109/lnet.2024.3490954
- Citations
- 5
- Primary Topic
- IoT and Edge/Fog Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.74