Decentralized Inference with Graph Neural Networks in Wireless Communication Systems

2021/04/19 by Mengyuan Lee, Guanding Yu, Lee, Mengyuan +3 · 4 citations
Computer Science · #Advanced Graph Neural Networks #Cooperative Communication and Network Coding #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2104.09027

openalex publication_date 2021/04/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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. 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.

Citations

• Unifying local and non-local signal processing with graph CNNs
• Adversarial Attack on Graph Structured Data
• Graph Neural Networks: A Review of Methods and Applications
• Deep Reinforcement Learning Based Resource Allocation for V2V Communications
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
• Provable defenses against adversarial examples via the convex outer adversarial polytope
• Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
• Certifiable Robustness to Graph Perturbations
• GraphSAC: Detecting anomalies in large-scale graphs
• Federated Learning in Mobile Edge Networks: A Comprehensive Survey
• A Comprehensive Survey on Graph Neural Networks

Cited by

• AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives
• An Overview on the Application of Graph Neural Networks in Wireless Networks
• Graph Attention Network for Optimal User Association in Wireless Networks
• Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach

Related

• Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks
• Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
• Graph Neural Networks for Wireless Communications: From Theory to Practice
• Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks
• Graph Neural Networks Meet Wireless Communications: Motivation, Applications, and Future Directions