Graph Neural Network Based Interference Estimation for Device-to-Device Wireless Communications
Document Type
Conference Proceeding
Date of Original Version
7-18-2021
Abstract
This paper concerns interference estimation problem for device-to-device (D2D) communication networks. In the considered system, D2D users share common spectrum resources, such that the D2D links have interference with each other. To achieve effective interference management, it is necessary to have an accurate understanding of the interference relationship of the D2D devices, which is difficult since the locations and the number of mobile devices can vary over time. In this paper, we formulate an interference estimation problem for the D2D communication network where the D2D users change over time. Our objective is to get accurate estimations for the interference suffered by a generic D2D link and for the interference a D2D link introduces on other users. We propose a graph convolutional neural network (GCN) based estimation model which can estimate the interference of a D2D link based on the location information of the corresponding D2D pairs. Simulation results show the performance of our method.
Publication Title, e.g., Journal
Proceedings of the International Joint Conference on Neural Networks
Volume
2021-July
Citation/Publisher Attribution
Jiang, He, Lusi Li, Zhenhua Wang, and Haibo He. "Graph Neural Network Based Interference Estimation for Device-to-Device Wireless Communications." Proceedings of the International Joint Conference on Neural Networks 2021-July, (2021). doi: 10.1109/IJCNN52387.2021.9534202.