  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.
