Interference Coordination Leveraging Weighted Graph Convolutional Network

Inter-cell interference poses a significant challenge to the performance and reliability of cellular networks due to the complex spatial and temporal relationships between network nodes. Addressing this issue requires accurate prediction and assessment of interference. This paper presents a novel solution leveraging the strengths of a weighted graph convolutional network (WGCN) combined with graph coloring techniques. Specifically, we propose a WGCN-based interference estimation model to accurately derive the real-time inter-cell interference. Then, a graph multi-coloring problem is considered for the interference coordination. To address the color collision between cells and the color (i.e. spectrum resources) requirement of individual cells in the graph coloring problem, we propose a WGCN-assisted graph multi-coloring (WGCN-GMC) algorithm to allocate spectrum resources rationally. Simulation results demonstrate that our approach significantly enhances interference coordination, and achieves an impressive average improvement of 58.2 % compared to the traditional GMC algorithm leading to improved overall network performance.