Customized Slicing for 6G: Enforcing Artificial Intelligence on Resource Management

Next generation wireless networks are expected to support diverse vertical industries and offer countless emerging use cases. To satisfy stringent requirements of diversified services, network slicing is developed, which enables service-oriented resource allocation by tailoring the infrastructure network into multiple logical networks. However, there are still some challenges in cross-domain multi-dimensional resource management for end-to-end (E2E) slices under the dynamic and uncertain environment. Trading off the revenue and cost of resource allocation while guaranteeing service quality is significant to tenants. Therefore, this article introduces a hierarchical resource management framework, utilizing deep reinforcement learning in admission control of resource requests from different tenants and resource adjustment within admitted slices for each tenant. In particular, we first discuss the challenges in customized resource management of 6G. Second, the motivation and background are presented to explain why artificial intelligence (AI) is applied in resource customization of multi-tenant slicing. Third, E2E resource management is decomposed into two problems, multi-dimensional resource allocation decision based on slice-level feedback, and real-time slice adaption aimed at avoiding service quality degradation. Simulation results demonstrate the effectiveness of AI-based customized slicing. Finally, several significant challenges that need to be addressed in practical implementation are investigated.

Automated summary

This article introduces a hierarchical resource management framework utilizing deep reinforcement learning for admission control of resource requests and resource adjustment within slices. It discusses challenges in customized resource management of 6G, the application of artificial intelligence in multi-tenant slicing, and decomposition of end-to-end resource management into allocation and adaptation problems. Simulation results show the effectiveness of AI-based customized slicing, while also highlighting significant challenges in practical implementation.