This repository is the Python implementation of paper "Robust Beamforming with Gradient-based Liquid Neural Network".
English version : NotImplemented.
Chinese version : Zhihu.
main.py: The main function. Can be directly run to get the results.
utils.py: This file contains the util functions. It also contains definition of system params.
net.py: This file defines and declares the GLNN and the params.
CSIdyn64.mat: An example of dataset for trial run.
requirements.txt: The requirement of the recommended environment.
Should you find this work beneficial, kindly grant it a star!
To keep abreast of our research, please consider citing:
Xinquan Wang, Fenghao Zhu, Chongwen Huang, Ahmed Alhammadi, Faouzi Bader, Zhaoyang Zhang, Chau Yuen, Merouane Debbah, "Robust Beamforming with Gradient-based Liquid Neural Network," IEEE Wireless communications Letters.
@article{glnn,
title={Robust Beamforming with Gradient-based Liquid Neural Network},
author={Xinquan Wang and Fenghao Zhu and Chongwen Huang and Ahmed Alhammadi and Faouzi Bader and Zhaoyang Zhang and Chau Yuen and M{\'e}rouane Debbah},
journal={IEEE Wirel. Commun. Lett.},
year={2024}
}Due to the size limitation of the files, bigger dataset is available here.
We are excited to announce a novel method that utilizes Manifold Learning to optimize spectrum efficiency in beamforming in RIS-aided MIMO systems.
Compared to baseline, it can speed up the convergence by 23 times and achieves a stronger robustness!
See GMML for more information!