Awesome

UWMMSE-MIMO

Tensorflow implementation of Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks (https://arxiv.org/abs/2304.00446)

Overview

This library contains a Tensorflow implementation of Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks as presented in [1](https://arxiv.org/abs/2304.00446).

Dependencies

• python>=3.6
• tensorflow>=1.14: https://tensorflow.org
• tensorflow_addons
• numpy
• matplotlib

Structure

• main: Main code for running the experiments in the paper. Run as python3 main.py --datasetID {dataset ID} --tx_antennas {T} --rx_antennas {R} --expID {exp ID} --mode {mode} --unrolled_layers {L}. For ex. to train UWMMSE on dataset with ID set3 having 5 tx and 3 rx antennas, run python3 main.py --datasetID set3 --tx_antennas 5 --rx_antennas 3 --expID uwmmse --mode train --unrolled_layers 1. For best results, train with 1 unrolled layer and use atleast 3 unrolled layers at inference.
• 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, Verma G, Swami A, Segarra S. Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks. 
arXiv preprint arXiv:2304.00446 2023 Apr 02.

BibTeX format:

@article{chowdhury2023deep,
  title={Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks},
  author={Chowdhury, Arindam and Verma, Gunjan and Swami, Ananthram and Segarra, Santiago},
  journal={arXiv e-prints},
  year={2023}
}