An implementation of our paper Deep Residual Network Empowered Channel Estimation for IRS-Assisted Multi-User Communication Systems (accepted by IEEE Trans. Wireless Commun., doi: 10.1109/TWC.2021.3100148).
For more information, you can visit the Home Page of the first author.
Also, there is another similar work of us: Deep Residual Learning-Assisted Channel Estimation in Ambient Backscatter Communications (published at IEEE Wireless Commun. Lett.).
Please follow the instructions of keras.
Clone the repository: git clone https://github.com/XML124/CDRN-channel-estimation-IRS.git
- use the two .m files to generate the training dataset and test dataset.
- run the CDRN.py to realize the CDRN algorithm.
If you use our code or if our work is useful for your research, please use the following BibTeX entry:
@article{liu2020deepresidual,
title={Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications},
author={Liu, Chang and Liu, Xuemeng and Ng, Derrick Wing Kwan and Yuan, Jinhong},
journal={IEEE Trans. Wireless Commun.},
year={2021 [Early Access], doi: 10.1109/TWC.2021.3100148}
}
Chang Liu(chang.liu19@unsw.edu.au / changliu.wcom@gmail.com)
Xuemeng Liu (xuemeng.liu@sydney.edu.au)
Any comments or suggestions are welcome!