# Channel Estimation for Full-Duplex RIS-assisted HAPS Backhauling with Graph Attention Networks

arXiv: https://arxiv.org/abs/2010.12004 · HTML: https://mlsanity.com/paper/2010.12004/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [Graph-based Deep Learning for Communication Networks: A Survey](https://mlsanity.com/paper/2106.02533/) (2021)
- [Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training](https://mlsanity.com/paper/2302.04368/) (2023)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Attention Based Neural Networks for Wireless Channel Estimation](https://mlsanity.com/paper/2204.13465/) (2022)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [Learning to Estimate RIS-Aided mmWave Channels](https://mlsanity.com/paper/2107.12631/) (2021)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks](https://mlsanity.com/paper/2301.11166/) (2023)
- [Graph Attention Networks for Channel Estimation in RIS-assisted Satellite IoT Communications](https://mlsanity.com/paper/2104.00735/) (2021)
- [Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks](https://mlsanity.com/paper/2510.19731/) (2025)
- [Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation](https://mlsanity.com/paper/2109.05466/) (2021)

## Abstract (arXiv)

In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time-division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small-scale fading characteristics even if the training data do not include all these variations.