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.
翻译:正在开发开放无线电接入网络(ORAN),目的是实现接入民主化,降低未来移动数据网络的成本,支持网络服务,满足各种QOS要求,如大规模IoT和URLLC等。在ORAN,网络功能被分解成远程单位(RUs)、分布式单位(DUs)和中央单位(CUs),允许在商业-关闭-Shelf(COTS)部署上采用灵活的软件。此外,将可变RU要求绘图到地方移动边缘计算中心,供今后集中处理,将大大减少移动电话网络的电力消耗。在本文中,我们研究了ORCAN系统中的RU-DU资源分配问题,以2D bin包装问题为模型。建议采用深度强化学习的自我玩耍方法,实现高效的RU-DU资源管理,使用AlphaGo Zro受启发的神经蒙特-Carlo树搜索(MCTS)。关于代表 2D bin包装环境和实际站点数据的实验显示,自玩式学习战略为不同网络条件下的RUD的智能资源分配。