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

Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu

TL;DR

The vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models is provided and 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.

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.

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

TL;DR

The vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models is provided and 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.

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.
Paper Structure (58 sections, 2 equations, 9 figures, 1 table)

This paper contains 58 sections, 2 equations, 9 figures, 1 table.

Table of Contents

  1. Introduction
  2. Roadmap to 6G: Vision and Technologies
  3. Edge AI: Challenges and Solutions
  4. Edge AI Empowered 6G Networks
  5. Key Contributions
  6. Communication-efficient edge training
  7. Edge Learning Models and Algorithms
  8. Federated Learning
  9. Decentralized Learning
  10. Model Split Learning
  11. Distributed Reinforcement Learning
  12. Trustworthy Learning
  13. Wireless Techniques for Edge Training
  14. Over-the-Air Computation
  15. Massive Access Techniques
  16. ...and 43 more sections

Figures (9)

  • Figure 1: Towards 6G: the evolution of use cases from 5G to 6G.
  • Figure 2: Roadmap to edge AI.
  • Figure 3: Edge AI empowered 6G networks: integrated sensing, communication, computation, and intelligence.
  • Figure 4: Edge learning models and architectures.
  • Figure 5: Edge learning modes in dynamic and adversarial environments.
  • ...and 4 more figures