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 optimize resource
allocation in these scenarios, we leverage deep reinforcement learning
techniques and introduce two model-free algorithms capable of monitoring the
network state and dynamically training 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.

基于容器虚拟化的云原生无线网络架构，应用深度强化学习算法来监控网络状态和动态分配资源，优化了网络分片和多接入边缘计算等场景的资源分配，大幅提高了网络效率。