基于协调多Agent深度强化学习的小区间切片资源划分
Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning
摘要：
网络切片使运营商能够为具有特定要求的各种服务配置虚拟网络实例。为了实现切片感知的无线电资源调度，需要动态切片资源划分来编排多小区切片资源并减轻小区间干扰。然而，由于复杂的单元间相互依赖性、切片间资源约束和特定于服务的要求，获得分析解决方案具有挑战性。在本文中，我们提出了一种多智能体深度强化学习（DRL）方法，该方法在保持资源容量约束的同时提高了最大-最小切片性能。我们设计了两个协调方案，允许分布式代理协调和减轻小区间干扰。所提出的方法进行了广泛的评估，在系统级模拟器。数值结果表明，所提出的方法与代理间的协调优于集中式方法的延迟和收敛。与基线方法相比，拟议方法的资源效率提高了两倍以上。
英文摘要：
Network slicing enables the operator to configure virtual network instances for diverse services with specific requirements. To achieve the slice-aware radio resource scheduling, dynamic slicing resource partitioning is needed to orchestrate multi-cell slice resources and mitigate inter-cell interference. It is, however, challenging to derive the analytical solutions due to the complex inter-cell interdependencies, interslice resource constraints, and service-specific requirements. In this paper, we propose a multi-agent deep reinforcement learning (DRL) approach that improves the max-min slice performance while maintaining the constraints of resource capacity. We design two coordination schemes to allow distributed agents to coordinate and mitigate inter-cell interference. The proposed approach is extensively evaluated in a system-level simulator. The numerical results show that the proposed approach with inter-agent coordination outperforms the centralized approach in terms of delay and convergence. The proposed approach improves more than two-fold increase in resource efficiency as compared to the baseline approach.
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。
