
# MU-MIMO datasets

This is the official repository of MU-MIMO datasets used in "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing" (ICDCS 2023).

`*-h_mat.npy` and `*-v_mat.npy` are input samples and targets, respectively.

If you have any questions about the datasets, please directly contact [`Niloofar Bahadori`](https://niloobahadori.github.io/) as she built both the real and synthetic datasets.

The code is available [here](https://github.com/yoshitomo-matsubara/split-beam).

## Citation

```bibtex
@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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TAGS
#size_categories-100K<n<1M #license-cdla-permissive-2.0 #region-us 


# MU-MIMO datasets

This is the official repository of MU-MIMO datasets used in "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing" (ICDCS 2023).

'*-h_mat.npy' and '*-v_mat.npy' are input samples and targets, respectively.

If you have any questions about the datasets, please directly contact 'Niloofar Bahadori' as she built both the real and synthetic datasets.

The code is available here.


# MU-MIMO datasets

This is the official repository of MU-MIMO datasets used in "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing" (ICDCS 2023).

'*-h_mat.npy' and '*-v_mat.npy' are input samples and targets, respectively.

If you have any questions about the datasets, please directly contact 'Niloofar Bahadori' as she built both the real and synthetic datasets.

The code is available here.
TAGS
#size_categories-100K<n<1M #license-cdla-permissive-2.0 #region-us 
 # MU-MIMO datasets

This is the official repository of MU-MIMO datasets used in "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing" (ICDCS 2023).

'*-h_mat.npy' and '*-v_mat.npy' are input samples and targets, respectively.

If you have any questions about the datasets, please directly contact 'Niloofar Bahadori' as she built both the real and synthetic datasets.

The code is available here.
passage: TAGS
#size_categories-100K<n<1M #license-cdla-permissive-2.0 #region-us 
# MU-MIMO datasets

This is the official repository of MU-MIMO datasets used in "SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing" (ICDCS 2023).

'*-h_mat.npy' and '*-v_mat.npy' are input samples and targets, respectively.

If you have any questions about the datasets, please directly contact 'Niloofar Bahadori' as she built both the real and synthetic datasets.

The code is available here.