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

by   Wanqing Guan, et al.

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. Particularly, 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.


page 2

page 4

page 5

page 6

page 7

page 13

page 14

page 15


Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing

The complexity of emerging sixth-generation (6G) wireless networks has s...

AI-Assisted Slicing-Based Resource Management for Two-Tier Radio Access Networks

While network slicing has become a prevalent approach to service differe...

Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks

Terahertz (THz) wireless networks are expected to catalyze the beyond fi...

Aiming in Harsh Environments: A New Framework for Flexible and Adaptive Resource Management

The harsh environment imposes a unique set of challenges on networking s...

SliceOps: Explainable MLOps for Streamlined Automation-Native 6G Networks

Sixth-generation (6G) network slicing is the backbone of future communic...

A Tutorial on AI-Enabled Non-Terrestrial Networks in 6G

Non-Terrestrial Networks (NTN) are expected to be a critical component o...

ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS

Artificial Intelligence (AI) and Deep Learning (DL) algorithms are curre...

Please sign up or login with your details

Forgot password? Click here to reset