Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs), which allows flexible software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the mapping of variable RU requirements to local mobile edge computing centres for future centralized processing would significantly reduce the power consumption in cellular networks. In this paper, we study the RU-DU resource assignment problem in an ORAN system, modelled as a 2D bin packing problem. A deep reinforcement learning-based self-play approach is proposed to achieve efficient RU-DU resource management, with AlphaGo Zero inspired neural Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing environment and real sites data show that the self-play learning strategy achieves intelligent RU-DU resource assignment for different network conditions.

中文翻译：

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开放式RAN网络中资源分配的自学学习策略

开放无线电接入网（ORAN）的开发旨在使接入民主化并降低未来移动数据网络的成本，以支持具有各种QoS要求的网络服务，例如大规模的IoT和URLLC。在ORAN中，网络功能可分解为远程单元（RU），分布式单元（DU）和中央单元（CU），从而可以在现成的商用（COTS）部署中使用灵活的软件。此外，将可变的RU要求映射到本地移动边缘计算中心以用于将来的集中处理将显着降低蜂窝网络中的功耗。在本文中，我们研究了以2D装箱问题为模型的ORAN系统中的RU-DU资源分配问题。为了实现有效的RU-DU资源管理，提出了一种基于深度强化学习的自我扮演方法。借助AlphaGo零启发式神经蒙特卡洛树搜索（MCTS）。在代表性的2D装箱环境和实际站点数据上进行的实验表明，自学习策略可以针对不同的网络条件实现智能的RU-DU资源分配。