GMML

This repository is the Python implementation of paper "Robust Beamforming for RIS-aided Communications: Gradient-based Manifold Meta Learning".

A simplified version, titled "Energy-efficient Beamforming for RIS-aided Communications: Gradient Based Meta Learning" and with manifold learning technique removed, was accepted for 2024 IEEE International Conference on Communications (ICC).

Blog

English version : Click here.

Chinese version : Click here.

Files in this repo

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.

Reference

Should you find this work beneficial, kindly grant it a star!

To keep abreast of our research, please consider citing:

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), June 9, 2024, pp. 5.98.

@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 = {June 9},
  pages = {5.98}
}