Homogeneous and Heterogeneous Graph Learning for Hybrid Beamforming in mmWave Systems

Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeter-wave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adapta...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on wireless communications Vol. 24; no. 10; pp. 8086 - 8100
Main Authors: Li, Yuhang, Lu, Yang, Zhang, Guangyang, Ai, Bo, Niyato, Dusit, Cui, Shuguang
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Antennas
Array signal processing
Beamforming
Communications systems
GAT
Graph neural networks
Graphs
HBF
HGAT
Machine learning
Matching pursuit algorithms
Millimeter wave communication
Millimeter waves
MISO
mmWave
Optimization
Resource management
Training
Wireless networks
ISSN:1536-1276, 1558-2248
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Hybrid analog and digital beamforming (HBF) is a cost-efficient technique to achieve high data rates in millimeter-wave (mmWave) communication systems. This paper applies the emerging graph neural networks (GNNs) to HBF by leveraging the topological information in wireless networks for better adaptation to dynamic environments. To address the issue of limited feature extraction capability of the existing single-type GNNs, such as node-GNN or edge-GNN, we model the mmWave communication systems as both homogeneous and heterogeneous graphs, and separate the HBF design into node- and edge-level subtasks. Then, the two graphs are presented by two novel models based on homogeneous graph attention network (GAT) and heterogeneous GAT (HGAT), respectively, and mapped to the desired power allocation, radio frequency precoder and baseband precoder. Both the proposed GAT and HGAT are generalizable in the user variation scenarios, while the HGAT is also generalizable in antenna variation scenarios through the incorporation of a complex embedding layer. Furthermore, we introduce a constraint adaptive layer in the GAT and HGAT to ensure feasible outputs. Extensive numerical results based on the public dataset DeepMIMO are provided to evaluate the GAT and HGAT. The proposed approaches generally outperform existing baselines in terms of adaptability to system settings and generalizability to (unseen) problem parameters/sizes, while the HGAT can even achieve faster and better inference than traditional optimization approaches.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3564290