Self-play learning strategies for resource assignment in Open-RAN networks

Abstract

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 centralised 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. Comparing with baseline methods, including a heuristic virtual resource allocation algorithm, the Lego heuristic algorithm and the MCTS methods, the proposed approach achieves a performance gain between 5.70% to 12.95% in terms of resource utilisation efficiency.