Deep Reinforcement Learning Based Resource Allocation for Cloud Native Wireless Network
This addresses resource management problems for cloud native wireless network operators, though it appears incremental as it applies existing DRL techniques to specific use cases.
The paper tackles resource allocation challenges in cloud native wireless networks for 5G/6G by proposing deep reinforcement learning algorithms, demonstrating significant improvements in network efficiency through testbed validation.
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.