Graph Neural Networks based Resource Allocation in Heterogeneous Wireless Networks

Abstract

Graph neural networks(GNNs) have been developed to solve challenging resource allocation (RA) problems, which leads to hopeful results in small and simple communication networks. Due to the inevitable heterogeneity of modern networks, it motivated researchers to develop the heterogeneous graph neural networks (HetGNNs) model for the RA problem of heterogeneous networks. However, node features and edge features are usually ignored by the most extant deep models, limited the performance when the size of the hidden layer in the network is larger than that of the node and edge features. In this paper, the power control or beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks is focused, and proposed a HetGNN for the issue. Extensive simulations show that the proposed approach, matching or even outperforming state-of-the-art learning-based benchmarks.