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.

中文翻译：

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迈向 6G 中的自学习边缘智能


边缘智能，也称为边缘原生人工智能（AI），是一种专注于人工智能、通信网络和移动边缘计算无缝集成的新兴技术框架。它被认为是现有 5G 网络中缺失的关键组件之一，并被广泛认为是未来无线 6G 蜂窝系统最受追捧的功能之一。在本文中，我们确定了 6G 中边缘原生 AI 的关键要求和挑战。引入了一种基于自监督生成对抗网络的自学习架构，以证明通过网络边缘的自动数据学习和合成可以实现潜在的性能改进。我们评估了我们提出的自学习架构在通过 5G 网络连接的大学校园班车系统中的性能。我们的结果表明，所提出的架构有潜力识别和分类边缘计算网络中出现的未知服务。还讨论了支持自学习的 6G 边缘智能的未来趋势和关键研究问题。