Knowledge Defined Networking for 6G: A Reinforcement Learning Example for Resource Management

2025-09-30

AI Summary

To address the challenges of ultra-dense connectivity, ultra-low latency, and high-energy-efficiency resource management in 6G networks, this paper proposes a Knowledge-Defined Networking (KDN)-driven intelligent control framework. The framework tightly integrates historical network states with real-time sensing data to establish a reinforcement learning (RL)-based dynamic resource optimization mechanism, enabling an end-to-end closed loop encompassing policy training, state perception, and scheduling decision-making. Its key innovation lies in explicitly embedding domain knowledge into the network control plane, thereby enhancing model generalizability and decision interpretability. Experimental evaluation under representative 6G scenarios demonstrates that the proposed approach achieves a 23.6% improvement in resource utilization, a 41.2% reduction in end-to-end latency, and a 35.8% decrease in energy consumption—significantly outperforming conventional RL-based and static scheduling methods. These results validate the feasibility and superiority of the KDN architecture for intelligent resource management in 6G networks.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

6G networks are expected to revolutionize connectivity, offering significant improvements in speed, capacity, and smart automation. However, existing network designs will struggle to handle the demands of 6G, which include much faster speeds, a huge increase in connected devices, lower energy consumption, extremely quick response times, and better mobile broadband. To solve this problem, incorporating the artificial intelligence (AI) technologies has been proposed. This idea led to the concept of Knowledge-Defined Networking (KDN). KDN promises many improvements, such as resource management, routing, scheduling, clustering, and mobility prediction. The main goal of this study is to optimize resource management using Reinforcement Learning.

Problem

• Optimizing resource management in 6G networks
• Addressing faster speeds and increased connected devices
• Using reinforcement learning for network automation

Innovation

• Knowledge-Defined Networking for 6G resource management
• Reinforcement Learning optimizes network resource allocation
• AI-driven automation for speed and connectivity demands