Predictive Temporal Graph Neural Networks for Power and Spectrum Allocation in D2D Networks

In this paper, we address the challenge of power control and spectrum allocation in Device-to-Device (D2D) networks with the aim to maximize the long-term average network rate. The inherent time-varying nature of wireless channels, caused by user mobility, complicates radio resource management (RRM) in such networks. address this concern, in this study, we propose a novel two-level solution that integrates Channel State Information (CSI) prediction with a Graph Neural Network (GNN)-based RRM model. On the first level, we develop an attention-based recurrent neural network (RNN) to predict future CSIs using historical data. On the second level, we transform the past, current, and predicted CSIs into multiple graph structures. We then design a GNN-based RRM model that captures the geometric properties of the CSIs across the following three dimensions: within resource blocks (RBs), between RBs, and over time (both forward and backward). Specifically, the proposed model employs message passing within RBs to capture local interference patterns, between RBs to optimize spectral resource allocation, and across temporal states to use temporal dependencies. The results of our simulation demonstrate that our method outperforms benchmark schemes across various wireless scenarios, achieving a higher network throughput while satisfying Quality of Service (QoS) constraints.