Toward Self-Learning Edge Intelligence in 6G

Yong Xiao, Guangming Shi, Yingyu Li, Walid Saad, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing. It has been considered to be one of the key missing components in the existing 5G network and is widely recognized to be one of the most sought after functions for tomorrow's wireless 6G cellular systems. In this article, we identify the key requirements and challenges of edge-native AI in 6G. A self-learning architecture based on self-supervised generative adversarial nets is introduced to demonstrate the potential performance improvement that can be achieved by automatic data learning and synthesizing at the edge of the network. We evaluate the performance of our proposed self-learning architecture in a university campus shuttle system connected via a 5G network. Our result shows that the proposed architecture has the potential to identify and classify unknown services that emerge in edge computing networks. Future trends and key research problems for self-learning-enabled 6G edge intelligence are also discussed.

Original languageEnglish (US)
Article number9311932
Pages (from-to)34-40
Number of pages7
JournalIEEE Communications Magazine
Volume58
Issue number12
DOIs
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Toward Self-Learning Edge Intelligence in 6G'. Together they form a unique fingerprint.

Cite this