Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning

This repository is accompanying the paper "Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning" (Bile Peng, Karl-Ludwig Besser, Ramprasad Raghunath and Eduard A. Jorswieck, IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 9313-9326, Dec. 2023. doi:10.1109/TWC.2023.3269770, arXiv:2212.12329).

File List

The following files are provided in this repository:

• main.py: Main file to train the model.
• test.py: Script to test the saved model on the validation data set.
• core.py: Core setup and functionality.
• rastrigin.py: Python module containing the benchmark functions.
• data/: Directory containing the training and validation data sets.
• results/: Directory containing the results model from the paper.

Usage

Make sure that you have Python3 and all necessary libraries installed on your machine.

Run python main.py with the following arguments to train the model:

• record: True if you want to save the tensorboard log and trained models in a folder named after date and time of the beginning of training, False otherwise.
• pmax: maximum transmit power.

Run python test.py to test the saved model on the validation data set.

Acknowledgements

This research was supported by the Federal Ministry of Education and Research Germany (BMBF) as part of the 6G Research and Innovation Cluster (6G-RIC) under Grant 16KISK031.

License and Referencing

This program is licensed under the GPLv3 license. If you in any way use this code for research that results in publications, please cite our original article listed above.

You can use the following BibTeX entry

@article{Peng2023,
  author = {Peng, Bile and Besser, Karl-Ludwig and Raghunath, Ramprasad and Jorswieck, Eduard A.},
  title = {Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning},
  journal = {IEEE Transactions on Wireless Communications},
  year = {2023},
  month = {12},
  volume = {22},
  number = {12},
  pages = {9313--9326},
  publisher = {IEEE},
  archiveprefix = {arXiv},
  eprint = {2212.12329},
  primaryclass = {eess.SP},
  doi = {10.1109/TWC.2023.3269770},
}