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Abstract: Task diversity is one of the biggest challenges for future sixth-generation (6G) networks. Taking the task as the center and driving the dynamic 6G radio access network (RAN) with artificial intelligence (AI) are necessary to accurately meet the personalized demands of users. However, AI can only configure the parameters of a monolithic RAN and cannot schedule the functions. The development trend of 6G RANs is to enhance dynamic capability and scheduling ease. In this paper, we propose a service-based RAN architecture that can deploy decoupled RAN functions and customize networks according to tasks. Protocol analysis shows that the interactive relationship between RAN control plane (CP) functions is complex and needs to be decoupled according to the principles of high cohesion and low coupling. Based on the graph theory rather than expert experience, we design a RAN decoupling scheme. The functional connection and interaction of the CP are represented by constructing an undirected weighted graph, followed by achieving decoupling of the CP through a minimum spanning tree. Then an integration decoupling scheme of a RAN-CN (core network) is introduced considering the duplicate and redundant functions of the RAN and CN. The granularity of decoupling in a service-based RAN is determined by analyzing the flexibility of decoupling, complexity of signaling, and processing delay. We find that it is more appropriate to decouple the RAN CP into four services. The integration decoupling of the RAN-CN resolves the technical bottleneck of low serial efficiency in the Ng interface, supporting AI-based global service scheduling.

面向开放和智能服务化RAN的服务解耦研究

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