Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

TL;DR

This paper envisions 6G networks integrating edge AI to enhance efficiency, privacy, and security by embedding AI capabilities at the network edge, supported by new design principles and system architectures.

Contribution

It proposes a comprehensive vision for scalable, trustworthy edge AI systems in 6G, including design principles, resource optimization, and system architecture for industrialization.

Findings

Design principles for wireless network integration with edge AI

Resource allocation optimization methods for edge AI

End-to-end system architecture supporting edge AI

Abstract

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.