This library contains a Tensorflow implementation of the paper Stability Analysis of Unfolded WMMSE for Power Allocation

Overview

UWMMSE-stability

Tensorflow implementation of Stability Analysis of UWMMSE

Overview

This library contains a Tensorflow implementation of the paper Stability Analysis of Unfolded WMMSE for Power Allocation[1].

Dependencies

Structure

  • main: Main code for generating dataset and training/evaluating UWMMSE model. Run as python3 main.py [dataset ID] [exp ID] [mode]. Eg., to train UWMMSE on dataset with ID set3, run python3 main.py set3 uwmmse train. Generates dataset with given ID if not already present.
  • validate: Plot figures 1(a) & 1(b) in the paper. Run as python3 main.py [dataset ID]. Eg., to run on dataset with ID set3, run python3 validate.py set3
  • model: Defines the UWMMSE model.
  • data: should contain your dataset in folder {dataset ID}.
  • models: Stores trained models in a folder with same name as {datset ID}.
  • results: Stores results in a folder with same name as {datset ID}.

Usage

Please cite [1] in your work when using this library in your experiments.

Feedback

For questions and comments, feel free to contact Arindam Chowdhury.

Citation

[1] Chowdhury A, Gama F, Segarra S. Stability Analysis of Unfolded WMMSE for Power Allocation. 
arXiv preprint arXiv:2110.07471 2021 Oct 14.

BibTeX format:

@article{chowdhury2021stability,
  title={Stability Analysis of Unfolded WMMSE for Power Allocation},
  author={Chowdhury, Arindam and Gama, Fernando and Segarra, Santiago},
  journal={arXiv e-prints},
  pages={arXiv--2110},
  year={2021}
}


Owner
Arindam Chowdhury
PhD Student
Arindam Chowdhury
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