Graph Embedding Multi-Agent Reinforcement Learning for Traffic Load Balancing in ORAN

Abstract

Traffic load balancing is a key near-real-time control function in dense and heterogeneous radio access networks, where uneven and time-varying traffic distributions can severely degrade resource utilisation and quality of service. Within the Open Radio Access Network (O-RAN) architecture, cell individual offset (CIO) control provides an effective mechanism to steer user handovers for load balancing. However, existing rule-based and centralised deep reinforcement learning (DRL) approaches suffer from limited adaptability and poor scalability, due to static assumptions, excessive signalling overhead, and the exponential growth of the joint action space. To address these challenges, this paper proposes a graph-based multi-agent reinforcement learning (MARL) framework with centralised training and distributed execution for CIO-based load balancing in O-RAN. Distributed actors perform decentralised near-real-time CIO control at individual RAN, while a centralised critic exploits graph-structured representations of inter-cell interactions during training. Graph attention mechanisms are employed to capture the heterogeneous and time-varying influence of neighbouring cells, improving learning stability and scalability. Simulation results demonstrate that the proposed approach achieves better performance than existing baselines.