2023 journal article
Decentralized Inference With Graph Neural Networks in Wireless Communication Systems
IEEE TRANSACTIONS ON MOBILE COMPUTING, 22(5), 2582–2598.
author keywords: Wireless communication; Robustness; Training; Predictive models; Data models; Resource management; Prediction algorithms; Machine learning; graph neural network; decentralized algorithm; retransmission; robustness
TL;DR:
This paper develops a methodology to verify whether the predictions are robust, and analyzes and enhances the robustness of the decentralized GNN during the inference stage in different wireless communication systems, and proposes novel retransmission mechanisms.
(via Semantic Scholar)
arxiv.org (repository)
Source: Web Of Science
Added: May 30, 2023
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 during the inference stage 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 during the inference stage in different wireless communication systems in this paper. Specifically, using a GNN binary classifier as an example, we first develop a methodology to verify whether the predictions are robust. Then, we analyze the performance of the decentralized GNN binary classifier in both uncoded and coded wireless communication systems. To remedy imperfect wireless transmission and enhance the prediction robustness, we further propose novel retransmission mechanisms for the above two communication systems, respectively. Through simulations on the synthetic graph data, we validate our analysis, verify the effectiveness of the proposed retransmission mechanisms, and provide some insights for practical implementation.
