Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

Minrui Xu, Hongyang Du, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shiwen Mao, Zhu Han, Abbas Jamalipour, Dong In Kim, Xuemin Shen, Victor C.M. Leung, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks.

Original languageEnglish (US)
Pages (from-to)1127-1170
Number of pages44
JournalIEEE Communications Surveys and Tutorials
Volume26
Issue number2
DOIs
StatePublished - 2024
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Keywords

  • AI training and inference
  • AIGC
  • Internet technology
  • communication and networking
  • generative AI
  • mobile edge networks

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