Graph Convolution Network Based State Space Model for Wireless Traffic Prediction

With the rapid advancement of communication networks, wireless traffic prediction plays a pivotal role in resource allocation and energy management. However, due to the complex spatio-temporal characteristics inherent in real-world traffic data, capturing its intrinsic features accurately has proven to be challenging, leading to unsatisfactory prediction accuracy. In this paper, we propose a Graph Convolution Network based State Space Model (GSSM) aimed at predicting call detail records wireless traffic. We utilize both fixed and adaptive weighted matrices to learn the complex spatial dependencies among base stations. The graph convolution network is conducted to accurately estimate the parameters of the state space model. Additionally, we employ a mixture Gaussian hypothesis to provide more flexibility for the state space model when predicting traffic data. Our experimental results demonstrate that the proposed method outperforms the baseline methods. Furthermore, we conduct extensive visualization, ablation and parameter analysis experiments to confirm the effectiveness of our proposed approach.