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!