This repository is the Python implementation of paper "Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning", which has been accepted by IEEE Transactions on Wireless Communications 2024
A simplified version, titled "Energy-efficient Beamforming for RIS-aided Communications: Gradient Based Meta Learning" and with manifold learning technique removed, has been accepted for 2024 IEEE International Conference on Communications (ICC).
English version : Click here.
Chinese version : Click here.
main.py: The main function. Can be directly run to get the results.
utils.py: This file contains the util functions, including the intialization functions and calculation function of spectral efficiency. It also contains definition of system params.
net.py: This file defines and declares the neural networks and their params.
TWC_Paper.pdf: This file is the PDF file of the paper.
Should you find this work beneficial, kindly grant it a star!
To follow our research, please consider citing:
F. Zhu et al., "Robust Beamforming for RIS-Aided Communications: Gradient-Based Manifold Meta Learning," in IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 15945-15956, Nov. 2024.
X. Wang, F. Zhu, Q. Zhou, Q. Yu, C. Huang, A. Alhammadi, Z. Zhang, C. Yuen, and M. Debbah, "Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning," in Proc. of the 2024 IEEE International Conference on Communications (ICC), Jun. 9, 2024, pp. 3464-3469.
@ARTICLE{Zhu2024GMML,
author={Zhu, Fenghao and Wang, Xinquan and Huang, Chongwen and Yang, Zhaohui and Chen, Xiaoming and Alhammadi, Ahmed and Zhang, Zhaoyang and Yuen, Chau and Debbah, Mérouane},
journal={IEEE Transactions on Wireless Communications},
title={Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning},
year={2024},
volume={23},
number={11},
pages={15945-15956},
keywords={Reconfigurable intelligent surfaces;meta learning;manifold learning;gradient;beamforming},
doi={10.1109/TWC.2024.3435023}}
@inproceedings{Wang2024EnergyEfficient,
author = {X. Wang and F. Zhu and Q. Zhou and Q. Yu and C. Huang and A. Alhammadi and Z. Zhang and C. Yuen and M. Debbah},
title = {{Energy-efficient Beamforming for RISs-aided Communications: Gradient Based Meta Learning}},
booktitle = {Proc. of the 2024 IEEE International Conference on Communications (ICC)},
year = {2024},
date = {Jun. 9},
pages = {3464-3469}
}
We are excited to announce a novel method that utilizes linear approximations of ODE-based neural networks to optimize sum rate in beamforming in mmWave MIMO systems.
Compared to baseline, it only uses 1.6% of time to optimize and achieves a significantly stronger robustness!
See GLNN for more information!