论文标题
智能干预管理XAPP使用深度强化学习
Smart Interference Management xApp using Deep Reinforcement Learning
论文作者
论文摘要
干扰仍然是蜂窝无线电访问网络（RAN）部署中的关键限制因素。有效的，数据驱动的，自我适应的无线电资源管理（RRM）解决方案对于应对干扰至关重要，从而达到所需的性能水平，尤其是在细胞边缘。在未来的网络体系结构中，运行近实时应用程序（称为XAPPS）运行的智能控制器（RIC）被认为是启用RRM的潜在组件。在本文中，基于深度强化学习（RL）XAPP，为智能干扰管理提出了联合的子带掩蔽和功率管理。子带资源掩盖问题被提出为马尔可夫决策过程（MDP），可以使用深层RL近似策略功能来解决，以避免使用常规表格的基于表格的方法的计算和存储成本极高。开发的XAPP在存储和计算中都是可扩展的。仿真结果证明了所提出的方法比分散的基准相对于细胞中心和细胞边缘用户率，能源效率和计算效率之间的权衡。
Interference continues to be a key limiting factor in cellular radio access network (RAN) deployments. Effective, data-driven, self-adapting radio resource management (RRM) solutions are essential for tackling interference, and thus achieving the desired performance levels particularly at the cell-edge. In future network architecture, RAN intelligent controller (RIC) running with near-real-time applications, called xApps, is considered as a potential component to enable RRM. In this paper, based on deep reinforcement learning (RL) xApp, a joint sub-band masking and power management is proposed for smart interference management. The sub-band resource masking problem is formulated as a Markov Decision Process (MDP) that can be solved employing deep RL to approximate the policy functions as well as to avoid extremely high computational and storage costs of conventional tabular-based approaches. The developed xApp is scalable in both storage and computation. Simulation results demonstrate advantages of the proposed approach over decentralized baselines in terms of the trade-off between cell-centre and cell-edge user rates, energy efficiency and computational efficiency.
