Deep Learning-Based GNN-Attention Framework for Near-Field Channel Modeling and Beamforming in Sixth-Generation Wireless Systems: A Simulation Study
- DOI
- 10.3791/72011
- Published
- 2026-09-01
- Container
- Journal of Visualized Experiments
- Publisher
- MyJove Corporation
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3791/72011,
title = {Deep Learning-Based GNN-Attention Framework for Near-Field Channel Modeling and Beamforming in Sixth-Generation Wireless Systems: A Simulation Study},
author = {WeiHui Zhou and ShuXue Ding and PingPing Zeng},
year = {2026},
journal = {Journal of Visualized Experiments},
doi = {10.3791/72011},
url = {https://doi.org/10.3791/72011}
}RIS
TY - JOUR TI - Deep Learning-Based GNN-Attention Framework for Near-Field Channel Modeling and Beamforming in Sixth-Generation Wireless Systems: A Simulation Study AU - WeiHui Zhou AU - ShuXue Ding AU - PingPing Zeng PY - 2026 JO - Journal of Visualized Experiments DO - 10.3791/72011 UR - https://doi.org/10.3791/72011 ER -
APA
Zhou, W., Ding, S., & Zeng, P. (2026). Deep Learning-Based GNN-Attention Framework for Near-Field Channel Modeling and Beamforming in Sixth-Generation Wireless Systems: A Simulation Study. Journal of Visualized Experiments. https://doi.org/10.3791/72011
Source records
- crossref · retrieved 2026-09-17T19:23:12.116Z