Deploying On-device AIGC Inference Services in 6G via Optimal MEC-device Offloading

Changshi Zhou, Weiqi Liu, Tao Han, Nirwan Ansari

Research output: Contribution to journalArticlepeer-review

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 languageEnglish (US)
JournalIEEE Networking Letters
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Information Systems
  • Communication
  • Hardware and Architecture

Keywords

  • 6G
  • AIGC
  • ChatGPT
  • Constrained Reinforcement Learning
  • LLM
  • MEC
  • On-device Computing

Fingerprint

Dive into the research topics of 'Deploying On-device AIGC Inference Services in 6G via Optimal MEC-device Offloading'. Together they form a unique fingerprint.

Cite this