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[ICDCS 2023] "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing"

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SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing

This is the official repository of our paper "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing" presented at ICDCS 2023.

Datasets

The datasets used in the paper are hosted at Hugging Face Datasets.
You can use the following commands to download the datasets:

mkdir -p ./resource/datasets/
git lfs install
git clone https://huggingface.co/datasets/yoshitomo-matsubara/mu-mimo ./resource/datasets/

Important Notice

If you have any questions regarding MATLAB and/or datasets, please directly contact Niloofar Bahadori as she provided MATLAB code and Python code to wrap the MATLAB code, set up the MATLAB environment, and created the datasets.

Setup

  • Python 3.8
  • MATLAB 2021R
  • conda
conda env create -f environment.yml
~/anaconda3/bin/pip3 install -r requirements.txt --user

Run

Train models

Use scripts under scripts/
e.g.,

sh scripts/2x2-20mhz/env1-batch.sh
sh scripts/2x2-20mhz/env2-batch.sh

Test models with post-training quantization

Use scripts under scripts-quantization/
e.g.,

sh scripts-quantization/2x2-20mhz/env1-batch.sh
sh scripts-quantization/2x2-20mhz/env2-batch.sh

Citation

@inproceedings{bahadori2023splitbeam,
  title={{SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing}},
  author={Bahadori, Niloofar and Matsubara, Yoshitomo and Levorato, Marco and Restuccia, Francesco},
  booktitle={2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)},
  pages={864--874},
  year={2023},
  organization={IEEE}
}

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[ICDCS 2023] "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing"

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