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 (GANs) is introduced to
\blu{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.

本文旨在探讨 6G 边缘本地人工智能网络中的关键需求和挑战，并介绍了一种基于自我监督生成对抗网络的自学习体系结构，可以在网络边缘实现自动数据学习和合成，实现较大的性能提升。最后，讨论了自学习可实现的未来趋势和关键研究问题。