Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

Understand

Graph neural network (GNN) is an efficient neural network model for graph data and is widely used in different fields, including wireless communications.

• Different from other neural network models, GNN can be implemented in a decentralized manner with information exchanges among neighbors, making it a potentially powerful tool for decentralized control in wireless communication systems.
• The main bottleneck, however, is wireless channel impairments that deteriorate the prediction robustness of GNN.
• To overcome this obstacle, we analyze and enhance the robustness of the decentralized GNN in different wireless communication systems in this paper.