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Next generation wireless networks are expected to support diverse verticalindustries and offer countless emerging use cases. To satisfy stringentrequirements of diversified services, network slicing is developed, whichenables service-oriented resource allocation by tailoring the infrastructurenetwork into multiple logical networks. However, there are still somechallenges in cross-domain multi-dimensional resource management for end-to-end(E2E) slices under the dynamic and uncertain environment. Trading off therevenue and cost of resource allocation while guaranteeing service quality issignificant to tenants. Therefore, this article introduces a hierarchicalresource management framework, utilizing deep reinforcement learning inadmission control of resource requests from different tenants and resourceadjustment within admitted slices for each tenant. Particularly, we firstdiscuss the challenges in customized resource management of 6G. Second, themotivation and background are presented to explain why artificial intelligence(AI) is applied in resource customization of multi-tenant slicing. Third, E2Eresource management is decomposed into two problems, multi-dimensional resourceallocation decision based on slice-level feedback and real-time slice adaptionaimed at avoiding service quality degradation. Simulation results demonstratethe effectiveness of AI-based customized slicing. Finally, several significantchallenges that need to be addressed in practical implementation areinvestigated.

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