Artificial Intelligence-Enabled Intelligent 6G Networks

Abstract

With the rapid development of smart terminals and infrastructures, as well as diversified applications (e.g., virtual and augmented reality, remote surgery and holographic projection) with colorful requirements, current networks (e.g., 4G and upcoming 5G networks) may not be able to completely meet quickly rising traffic demands. Accordingly, efforts from both industry and academia have already been put to the research on 6G networks. Recently, artificial intelligence (AI) has been utilized as a new paradigm for the design and optimization of 6G networks with a high level of intelligence. Therefore, this article proposes an AI-enabled intelligent architecture for 6G networks to realize knowledge discovery, smart resource management, automatic network adjustment and intelligent service provisioning, where the architecture is divided into four layers: intelligent sensing layer, data mining and analytics layer, intelligent control layer and smart application layer. We then review and discuss the applications of AI techniques for 6G networks and elaborate how to employ the AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. Moreover, we highlight important future research directions and potential solutions for AI-enabled intelligent 6G networks, including computation efficiency, algorithms robustness, hardware development and energy management.

Published in: IEEE Network. https://doi.org/10.1109/MNET.011.2000195

Cited by in corpus (25)

• Rate-Splitting Multiple Access: Fundamentals, Survey, and Future Research Trends
• Security and privacy for 6G: A survey on prospective technologies and challenges
• Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G
• Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities
• A Survey on Cellular-connected UAVs: Design Challenges, Enabling 5G/B5G Innovations, and Experimental Advancements
• At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence
• Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
• UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks
• 6G: The Intelligent Network of Everything
• Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
• Deep Reinforcement Learning for RIS-aided Multiuser Full-Duplex Secure Communications with Hardware Impairments
• Internet of Intelligence: A Survey on the Enabling Technologies, Applications, and Challenges
• Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey
• Applications of Game Theory in Vehicular Networks: A Survey
• Tactile based Intelligence Touch Technology in IoT configured WCN in B5G/6G-A Survey
• A Survey on Privacy for B5G/6G: New Privacy Challenges, and Research Directions
• Federated Deep Reinforcement Learning for RIS-Assisted Indoor Multi-Robot Communication Systems
• A Comprehensive Survey of 6G Wireless Communications
• Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management
• Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learning
• Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
• Air-Ground Collaborative Mobile Edge Computing: Architecture, Challenges, and Opportunities
• 6G Vision: An Ultra-Flexible Perspective
• Why white noise is not enough? On using radio front-end models while designing 6G PHY
• Proactive and AoI-aware Failure Recovery for Stateful NFV-enabled Zero-Touch 6G Networks: Model-Free DRL Approach