Towards Ubiquitous 6G Computing and Networking Convergence: Architecture and Mechanism for Cross-Domain Resource Coordination

2026-06-12

AI Summary

This work addresses the insufficient coordination of computing and communication resources in diverse 6G scenarios by proposing a hierarchical cross-domain resource协同 architecture that deeply embeds computational capabilities into mobile network infrastructure, enabling deep integration of communication and computing for service orchestration. The authors innovatively design a cross-domain orchestration mechanism based on hierarchical multi-agent reinforcement learning, transcending the traditional separation between cloud and network to support representative 6G applications such as immersive communication and integrated sensing and communication. Experimental results demonstrate that the proposed approach significantly reduces system energy consumption and improves task satisfaction, validating its efficiency and feasibility across multiple 6G use cases.

Abstract

The 6G network will support six major application scenarios, such as immersive communication, integrated AI and communication, and integrated sensing and communication. Many scenarios necessitate significant computational support. Moreover, user demands are becoming increasingly segmented, diverse, and personalized. Traditional network slicing alone is insufficient to meet the heterogeneous computing and networking demands of emerging service scenarios. Mobile computing network convergence (CNC) introduces a fundamentally different paradigm from the conventional cloud computing plus communication network model by deeply embedding computing resources into the mobile network infrastructure and enabling integrated computing-network services tailored to diverse user demands. In this article, we investigate orchestration architectures and mechanisms for CNC in 6G mobile networks. We begin by reviewing the evolution of CNC from a mobile network perspective and surveying existing studies, which we categorize according to mobile network architectures. Building on these insights, we propose a hierarchical, cross-domain coordination architecture and an orchestration mechanism based on hierarchical multi-agent reinforcement learning. Performance evaluations demonstrate that the proposed architecture and mechanism significantly reduce system energy consumption while enhancing task satisfaction rate. Finally, we discuss open challenges and future research directions.

Problem

• 6G
• computing and networking convergence
• cross-domain resource coordination
• heterogeneous service demands
• network slicing

Innovation

• Computing-Network Convergence
• Cross-Domain Resource Coordination
• Hierarchical Multi-Agent Reinforcement Learning
• 6G Architecture
• Orchestration Mechanism