DeepSlicing：深度强化学习辅助的网络切片资源分配
DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
摘要：
网络切片使多个虚拟网络能够在同一物理基础设施上运行，以支持5G及更高级别的各种用例。然而，这些用例具有非常不同的网络资源需求，例如，通信和计算，以及各种性能指标，如延迟和吞吐量。为了有效地将网络资源分配给切片，我们提出了DeepSlicing，它集成了交替方向乘法器（ADMM）和深度强化学习（DRL）。DeepSlicing将网络切片问题分解为一个主问题和几个从问题。主问题采用凸优化方法求解，从问题采用DRL方法学习最优资源分配策略。通过网络仿真验证了该算法的性能。
英文摘要：
Network slicing enables multiple virtual networks run on the same physical infrastructure to support various use cases in 5G and beyond. These use cases, however, have very diverse network resource demands, e.g., communication and computation, and various performance metrics such as latency and throughput. To effectively allocate network resources to slices, we propose DeepSlicing that integrates the alternating direction method of multipliers (ADMM) and deep reinforcement learning (DRL). DeepSlicing decomposes the network slicing problem into a master problem and several slave problems. The master problem is solved based on convex optimization and the slave problem is handled by DRL method which learns the optimal resource allocation policy. The performance of the proposed algorithm is validated through network simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
