5 papers · 1 filter
KANsformer for Scalable Beamforming
Xinke Xie, Yang Lu, Chong-Yung Chi +3
This paper proposes an unsupervised deep-learning (DL) approach by integrating transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming fo…
Model-Based GNN Enabled Energy-Efficient Beamforming for Ultra-Dense Wireless Networks
Rongsheng Zhang, Yang Lu, Wei Chen +2
This paper investigates deep learning enabled beamforming design for ultra-dense wireless networks by integrating prior knowledge and graph neural network (GNN), named model-based…
GNN-Enabled Optimization of Placement and Transmission Design for UAV Communications
Qinyu Wang, Yang Lu, Wei Chen +3
This paper applies graph neural networks (GNN) in UAV communications to optimize the placement and transmission design. We consider a multiple-user multiple-input-single-output UAV…
AI-Empowered RIS-Assisted Networks: CV-Enabled RIS Selection and DNN-Enabled Transmission
Conggang Hu, Yang Lu, Hongyang Du +3
This paper investigates artificial intelligence (AI) empowered schemes for reconfigurable intelligent surface (RIS) assisted networks from the perspective of fast implementation. W…
Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation
Yang Lu, Yuhang Li, Ruichen Zhang +3
Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are…