To meet the increasing demand for mobile traffic in the 5G era, the deployment density and quantity of base stations (BSs) in wireless access networks have significantly increased. However, this has exacerbated the issue of energy consumption in mobile communication networks and exacerbates the magnitude of the energy saving issue in base stations. Consequently, the complexity of the co-coverage relationships among cells has greatly increased, making it more challenging to optimize the coordination among BSs.
Moreover, directly processing large-scale datasets is not feasibledue to memory constraints and other limitations. In light of the large-sca le BS energy-saving scenarios in 4G and 5G, we propose a combined approach that utilizes data-driven graph neural networks (GNNs) and reinforcement learning. First, we employ spectral clustering to partition the large-scale dataset into more manageable subsets of small-scale subgraphs. Subsequently, we use GNN encoders to encode the complex inter-cell coverage relationships within each small-scale subgraph. To address the problem of coordinated optimization among subgraphs, we employ the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. Finally, we address the information loss caused by data partitioning by transferring nodes between different subgraphs, enabling global energy optimization.
Our approach achieves a significant improvement in energy savings, with an over 60% enhancement in energy efficiency in a real dataset consisting of tens of thousands of cells.