Self-play Learning Strategies for Resource Assignment in Open-RAN Networks
This work addresses resource management in Open-RAN networks to reduce power consumption and support diverse QoS requirements, representing an incremental improvement by applying existing self-play methods to a specific domain problem.
The paper tackles the RU-DU resource assignment problem in Open-RAN networks by modeling it as a 2D bin packing problem and proposes a deep reinforcement learning-based self-play approach with AlphaGo Zero-inspired neural Monte-Carlo Tree Search, achieving intelligent resource assignment for various network conditions as shown in experiments on representative environments and real data.
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.