Efficient Deep Reinforcement Learning-Based Resource Allocation for Cloud Native Wireless Network

Lin Wang

doi:10.1109/TGCN.2025.3550599

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Efficient Deep Reinforcement Learning-Based Resource Allocation for Cloud Native Wireless Network

2025-12-01

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Lin Wang

Jiasheng Wu

张静静 (Jingjing Zhang) *

Yue Gao

DOI：10.1109/TGCN.2025.3550599

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Abstract

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Cloud native technology has revolutionized 5G beyond and 6G communication networks, offering unprecedented levels of operational automation, flexibility, and adaptability. However, the vast array of cloud native services and applications presents a new challenge in resource allocation for dynamic cloud computing environments. To tackle this challenge, we investigate a cloud native wireless architecture that employs container-based virtualization to enable flexible service deployment. We then study two representative use cases: network slicing and multi-access edge computing. To improve resource allocation and maximize utilization efficiency in these scenarios, we propose two deep reinforcement learning-based algorithms that enhance resource allocation efficiency and network resource utilization by leveraging comprehensive observational data to guide and refine the allocation policies. We validate the effectiveness of our algorithms in a testbed developed using Free5gc. Our findings demonstrate significant improvements in network efficiency, underscoring the potential of our proposed techniques in unlocking the full potential of cloud native wireless networks.

Keywords:

Cloud computing

Resource management

5G mobile communication

Network slicing

6G mobile communication

Wireless networks

Scalability

Deep reinforcement learning

Multi-access edge computing

Algorithm design and analysis

Network architecture