50 citations · 55 across the 11 of their papers we have counts for
7 papers · 1 filter
Cross-System Neural Precoder: Exploiting Structural Consistency for Fast Adaptation
Jia Guo, Chenyang Yang
Adapting learning-based precoding across different system configurations is challenging due to multiple types of variables and constraints. While large-scale neural networks have b…
When Attention is Beneficial for Learning Wireless Resource Allocation Efficiently?
Jia Guo, Chenyang Yang
Owing to the use of attention mechanism to leverage the dependency across tokens, Transformers are efficient for natural language processing. By harnessing permutation properties b…
Learning Precoding in Multi-user Multi-antenna Systems: Transformer or Graph Transformer?
Yuxuan Duan, Jia Guo, Chenyang Yang
Transformers have been designed for channel acquisition tasks such as channel prediction and other tasks such as precoding, while graph neural networks (GNNs) have been demonstrate…
Recursive GNNs for Learning Precoding Policies with Size-Generalizability
Jia Guo, Chenyang Yang
Graph neural networks (GNNs) have been shown promising in optimizing power allocation and link scheduling with good size generalizability and low training complexity. These merits…
Multidimensional Graph Neural Networks for Wireless Communications
Shengjie Liu, Jia Guo, Chenyang Yang
Graph neural networks (GNNs) have been shown promising in improving the efficiency of learning communication policies by leveraging their permutation properties. Nonetheless, exist…
A Model-based GNN for Learning Precoding
Jia Guo, Chenyang Yang
Learning precoding policies with neural networks enables low complexity online implementation, robustness to channel impairments, and joint optimization with channel acquisition. H…
Understanding the Performance of Learning Precoding Policy with GNN and CNNs
Baichuan Zhao, Jia Guo, Chenyang Yang
Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels. Yet previous works rarely…